The 2021 State of Housing in Charlotte Report

The State of Housing in Charlotte Report 2021

2

Contents Executive Summary

3

I. Introduction

4

A. The State of Housing in Charlotte

4

B. Geographic Scope

5

C. Data Sources

5

D. Organization of the Report

7

E. Sponsors

7

II. The Owner-Occupied Housing Market A.

9

B.

25

C.

28

III. Rental Housing Market A.

31

B.

39

C.

45

D.

51

8

30

IV. Conclusion and Next Steps

54

Appendix I: General Macroeconomic Overview

55

A.

Data Sources

55

B.

Population

56

C.

Income

63

D.

Housing Units

65

E.

Key Conclusions

73

Appendix II: Comparative Analysis

74

A.

Population and Population Growth

75

B.

Land Prices

77

C.

Median Home Prices

79

D.

Median Multiple Analysis

81

E.

Median Rents

83

F.

Price to Rent Ratio

84

G.

Cost-Burdens

86

H.

Summary

91

The State of Housing in Charlotte Report 2021

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Executive Summary This is the third annual State of Housing in Charlotte Report issued by the Childress Klein Center for Real Estate (CKCRE) at The University of North Carolina at Charlotte. Our goal with this report is to provide a comprehensive overview of the state of housing in Charlotte and the surrounding area and to examine how the markets have changed in the year since the first report was written. In this report, in particular, we seek to provide a complete overview of what happened in the region during the COVID-19 pandemic, which we consider as from January 2020 to September 2021. We also try to provide a long-term view of the dynamics of the housing market over the last two decades, that is, from 2001 to 2020.

We first summarize the pandemic housing market in the region as follows: •

House prices increased at an unprecedented rate during the pandemic. The median home prices in the Charlotte market increased at an annual rate of 16.3% from September 2020 to September 2021.

•

The supply of owner-occupied housing is extremely tight. In June 2021, the median days on the market fell to three days, and almost 60% of the houses sold are above their listing prices.

•

Houses with affordable prices are becoming extremely difficult to find. Only 4.4% of the houses sold are under $150,000, and only about 35% of the houses sold are under $300,000.

•

Middle-income housing affordability is becoming a significant challenge for the region. While the situation is alleviated by the low interest rate, the problem will become more severe when monetary policy tightens post-pandemic.

•

Rental rates increased dramatically during the pandemic. In 2021, the average effective rent increased by $198 (or 16.6%) per unit.

The main findings from our long-term analysis are: ● House price growth rates are accelerating in recent years. The average annual growth rate of median houses was 4.98% from 2010 to 2015, and 7.8% from 2015 to 2020. ● House price growth rates are much higher at the lower end than at the higher end in recent years. The 25th percentile of house prices increased at an annual rate of 8.37% from 2010-20, while the 75th percentile increased at a 4.31% annual rate.

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I. Introduction A. The State of Housing in Charlotte The Charlotte region continues to grow even during the pandemic. A pleasant climate, plentiful jobs, and a relatively low cost of living combine to make the region an attractive destination for many newcomers. During the pandemic, the Charlotte region became even more attractive as many people can work from home. As a result, the region’s population continues to grow rapidly. The U.S. Census Bureau estimates that, from July 1, 2019 to July 1, 2020, the population of the Charlotte region grew by 42,877 people, or a rate of roughly 118 people per day.1 The rapid growth inevitably leads to a persistent and strong demand for housing, which, combined with the struggles from the supply side during the pandemic, caused a dramatic increase in house prices. Furthermore, the region is also experiencing a rapid shift in the distribution of house prices. In particular, the prices at the lower end of the distribution have increased at a much higher rate than at the higher end, causing significant concerns for housing affordability in the region.

This is the third annual State of Housing in Charlotte Report issued by CKCRE. The first report was released in April 2019, and this second report was released in October 2020 because of a COVID-induced delay. We then decided to schedule the annual release of the report in the fall of each year. When the decision was made, we were hoping the COVID-19 pandemic would have been over by November 2021, so that this report can provide a complete analysis of the impact of the pandemic on the housing market. We were also hoping to use the release of the 2020 American Community Survey (ACS) data, usually scheduled in October, to gain more insights into the underlying changes in the economic conditions of the region. But unfortunately, the release of this data has been delayed, and we are not able to update part of the report.

Our goal with this report is to provide a comprehensive overview of the state of housing in Charlotte and the surrounding area and to examine how the markets have changed during the pandemic and over the last two decades. We continue to make this report a data-driven endeavor.

We use a wide variety of data sources, both public and proprietary, to show the state of the housing 1

Source: American Community Survey Demographic and Housing Estimates for 2019 and 2020. Data available at data.census.gov.

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markets. Throughout the report, we present data first and then, where appropriate, use that data to conclude as to what is happening in the marketplace.

B.

Geographic Scope

The scope of this report is broad. Geographically, the report focuses on Mecklenburg County and the seven counties that are physically adjacent to it. Specifically, this means Cabarrus, Gaston, Iredell, Lincoln, and Union counties in North Carolina, and Lancaster and York counties in South Carolina. Throughout the report, when we refer to the “Charlotte region,” this is the set of counties to

which

we

are

referring.

These

eight

counties

are

a

subset

of

the

U.

S. Census Bureau’s Charlotte-Gastonia-Concord Metropolitan Statistical Area (henceforth the “Charlotte MSA”). We chose to focus on the eight counties in the Charlotte region primarily because of data availability. These counties have the richest sets of data associated with them and represent the vast majority of the population and housing in the Charlotte MSA. Although the focus of the report is on those eight counties, we do use data for the entire MSA from time to time.

Specifically, we use MSA-level data when comparing Charlotte’s housing market against those of regional and national competitor cities. In all cases, we have tried to be clear as to the exact geographic area that we are discussing.

C.

Data Sources

This report uses seven primary data sources. These are as follows: 1. The Canopy Realtor® Association (Canopy) Multiple Listing Service (MLS) – The Canopy Realtor® Association has provided CKCRE with its database of MLS listings and sales from 2001 through the present. These data are proprietary, and CKCRE is only able to provide the summary information included in this report. These data are proprietary and CKCRE cannot provide the underlying data to the public. However, we will be making the summary statistics presented in this report available online on our website.

2. Metrostudy – CKCRE uses data obtained under license from Metrostudy. These data are proprietary, and CKCRE cannot provide the underlying data to the public.

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3. CoStar – CKCRE uses data obtained under license from CoStar to examine the apartment market. These data are proprietary, and CKCRE cannot provide the underlying data to the public.

4. U.S. Census Bureau – The U.S. Census Bureau provides a wide variety of publicly available data related to the population and housing across the entire country. We use data from the American Community Survey (ACS) and the American Housing Survey, as well as data from the 2010 Census in this report. Unless otherwise noted, data from the ACS come from the one-year estimates. In some cases, we use data tabulated by the Census Bureau and are available through the census data portal: (data.census.gov.)2 In other cases, we

have

used

data

from

the

Public

Use

Micro

Sample

(PUMS)

data:

www.census.gov/programs-surveys/acs/data/pums.html. All of these data are available from the various U.S. Census Bureau data portals. We are also using the Census Household

Impulse

Survey

Data

(www.census.gov/data/experimental-data-

products/household-pulse-survey.html ) to understand the impact of the pandemic on homeowners and renters.

5. U.S. Department of Housing and Urban Development (HUD) – We use two sets of data that are available from the HUD website: huduser.gov. The first is a set of income limits HUD uses when determining the eligibility for certain housing subsidies. The second is the database of low-income housing tax credit projects in the region. These data are publicly available from the HUD portal.

6. Federal Reserve Data – We use data on inflation and mortgage interest rates obtained from the Federal Reserve Economic Data (FRED) portal maintained by the St. Louis FED: fred.stlouisfed.org/. These data are available to the public.

2

Note that in our 2019 report we used data from the American Fact Finder web site, which was also a Census Bureau product. The Census Bureau has decommissioned that site and now only provides the data.census.gov site.

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D.

Organization of the Report

Because of the unavailability of the 2020 ACS data, we are forced to change the organization of this year’s report. Section II presents the analysis of the owner-occupied housing market; Section III presents the analysis of the rental housing market; and Section IV summarizes the report. To also provide the background information on macroeconomic trends, we provide the macro-overview of the region contained in our last report as an appendix to this report.

E.

Sponsors

This report has been made possible because of the financial support of a wide variety of interested housing participants. Those providing financial support include: ● Canopy Realtor® Association ● Crosland Southeast ● Evergreen Strategies ● Inlivian ● Piedmont Public Policy Institute ● True Homes We also wish to thank the Canopy Realtor® Association for its substantial data contribution.

Without this donation of data from the Carolina Multiple Listing Service, this project could not have been done. Finally, CKCRE thanks several key experts who have generously given their time and energy to discuss this report and our analysis. This includes: Mark Boyce with True Homes, Brenda Hayden with Keller Williams Realty, Fulton Meachem with Inlivian, Rob Nanfelt with the Real Estate Building and Industry Coalition, Tim Sittema with Crosland Southeast, and Landon Wyatt with Childress Klein Properties. The suggestions, counsel, and advice we have received from this group have been invaluable. Any errors or omissions, of course, are purely those of the authors and CKCRE.

II. The Owner-Occupied Housing Market We first examine the owner-occupied market. This market consists primarily of single-family detached homes but also includes attached units such as townhomes or condominiums. Because of the big differences between single-family and other owner-occupied housing markets, we

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separated these two markets in this report.3 Unless otherwise noted, we use Canopy MLS data for the analysis.

A.

The Single-Family Housing Market We start our analysis with the prices of single-family houses during the pandemic, that is, from

January 2020 to September 2021. We first look at the average and median house prices in the Charlotte region and in Mecklenburg County, as shown in Figure II.1. The median house price in the region increased from $273,500 in January 2020 to $366,314 in September 2021. The annual growth rate from September 2020 to September 2021 is an astounding 16.3%. To put this into perspective, the average annual growth rate during the last 10 years was 5.96%, as shown in Figure III.2. The average price is higher than the median price due to the skewness of the house prices.

The dynamics of the average price show a similar trend as the median price.

The dramatic increases in house prices could be driven by many factors. At the macroeconomic level, the unlimited quantitative easing programs implemented by the Federal Reserve substantially reduced mortgage interest rates. The average 30-year fixed-rate mortgage interest rate went from about 3.75% in January 2020 to 3% in September 2021. As the economy recovers from the pandemic and the worry about inflation intensifies, monetary policy will eventually tighten in the near future. The second factor is the extra demand for space spurred by working from home (and learning from home) during the pandemic. Some households either wanted to At this point, it is not clear whether all the work from home will be here to stay, but at least some form of it will remain, thus contributing to continued strong demand for housing. A third contributing factor is domestic migration. In particular, the region attracted 28,860 net domestic migration from July 1, 2019 to June 30, 2020. Although we do not have data since July 1, 2020, anecdotal evidence suggests that the Charlotte region continues to be one of the favorite locations of domestic migration. Work from home during the pandemic also allows people to move to relatively low-cost areas that provide a better quality of life. The relatively low cost of living of the Charlotte region makes it an attractive area for people to move to from high-cost areas, such as New York and California. While before the pandemic, businesses and people were already

3

This is different from our previous reports, where we analyzed the whole owner-occupied housing market.

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moving to Charlotte, the pandemic certainly accelerated the process. And we do expect that this trend will continue, maybe at a slower pace than what we experienced during the pandemic.

Figure II.1 Charlotte Region and Mecklenburg County Average and Median House Price January 2020- September 2021

To further understand the differences in the price dynamics among different counties, we further plot the annual price dynamics for each county in Figure II.3. Over the last two decades, Union County had the highest house price growth rate, especially in recent years.

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Figure II.2 Charlotte Region and Mecklenburg County Average and Median House Price 2001-2020

Figure II.3 Median Prices by County January 2020- September 2021

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The increases in house prices are driven by strong demand for housing. It is, however, not clear how the supply side has contributed to the dramatic increases in house prices. To gain a better understanding of the supply side, we plot the monthly listings and the monthly sales in Figure II.4.

Both listings and sales took a deep dive at the start of the pandemic in April 2020, but have since rebounded back. The monthly sales reached the historical high of 4,215 units in June 2021. An important observation is that the number of listings each month barely catches up with the number of sales each month, suggesting that the supply side is very tight. In particular, the number of listings fell short of the number of sales for most of 2020. To put this into perspective, we again plot the annual listings and sales from 2001 to 2020 in Figure II.5. Nothing similar ever happened, even during the booming years of the early 2000s.

Figure II.4 Listings and Sales January 2020- September 2021

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Figure II.5 Listings and Sales 2001- 2020

To further gauge the tightness of the housing market, we turn to the number of days on the market, that is, the number of days from when a house is listed to when the house is under contract.

We plot the monthly median days on the market in Figure II.6. Pre-COVID, the market was already pretty tight with the median days on the market of about 20-21 days. The pandemic turned the market extremely tight. In fact, the median days on the market dramatically decreased to about seven days in April 2020. As the pandemic continued to its second year, the median days on the market decreased to an astonishing three days, which was never seen before, as shown in Figure II.7. The market completely becomes a sellers’ market during the pandemic.

To further confirm that the housing market became a seller’s market during the pandemic, we then look at the percentage of houses that are sold above list prices. The results are presented in Figure II.8. Of all the houses sold in January 2020, only about 16% of them sold above the listing prices. However, the percentage kept increasing and reached a historic high of 59% in June and July 2021. This is again something that had never happened before. As shown in Figure II.9, the The State of Housing in Charlotte Report 2021

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percentage of houses sold above listing prices was mostly below 20% before 2020, even during the boom years in the early 2000s, except for 2017. These trends clearly show the hotness of the housing market and that the rapid increases in prices are driven by strong demand and a lack of supply.

Next, we turn to examine the full distribution of the prices to gain a deeper understanding of how the dramatic increases in house prices may have severely impacted housing affordability in the region. We first plot the house price distribution for houses sold from January 1, 2021 to September 30, 2021 in Figure II.10. Several observations stand out. The percent of houses sold under $150,000, often considered a reasonable price point for a starter home, is only 4.39%. Even considering a much higher price point, $300,000, the percent of houses sold under that price point is only about 35%. On the other hand, houses that sold for more than $1,000,000 account for 3.5%.

Even compared with 2020, as shown in Figure II.11, the changes are dramatic. In 2020, houses sold under $150,000 was 6.53%, and houses sold under $300,000 was 49%. The dramatic decrease in housing supply at the low end of the distribution raises serious concerns for housing affordability.

Taking a longer-term perspective, the change in house price distribution is even more dramatic.

We first show the distribution in 2010 in Figure II.12. It is evident that the distribution shifts much to the left. In particular, houses sold under $150,000 account for almost 45% of all houses sold in 2010; and houses under $300,000 account for almost 80%. Going back to 2001, the percent of houses sold under $150,000 was 53%, and the percent of houses sold under $300,000 was 89%, as shown in Figure II.13. One thing for sure is that the shift in distribution is accelerating over time as well.

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Figure II.6 Median Days on the Market January 2020- September 2021

Figure II.7 Median Days on the Market 2001-2020

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Figure II.8 Percent Sold at above Listing Price January 2020-September 2021

Figure II.9 Percent Sold at above Listing Price 2001-2021

Figure II.10 House Price Distribution The State of Housing in Charlotte Report 2021

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2021

Figure III.11 House Price Distribution 2020

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Figure II.12 House Price Distribution 2010

Figure III.13 House Price Distribution 2001

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To better understand the shift in the distribution over time, we plot the 10th, 75th, and 90th percentile of the prices from 2001 to 2020 in Figure II.14. Focusing on the lower end first, the 10th percentile did not increase at all from 2001 to 2010. And if anything, the 10th percentile actually decreased at an annual rate of 5% during that period and even decreased during the booming years of the early 2000s. However, the dynamics completely changed in the second half of the sample period. The 10th percentile increased at an annual rate of 12.3% from 2010 to 2020. The 25th percentile shows a similar trend over this period. On the other hand, the dynamics at the high end are very different. Both the 75th and 90th percentiles increased dramatically during the boom years of the early 2000s. And after the decline during the great recession, the high ends continued increasing, but at a slower speed than before the recession. The 75th percentile increased at an annual rate of 3.1% from 2001 to 2020 and at an annual rate of 4.31% from 2010 to 2020. The 90th percentile increased at an annual rate of 3.74% from 2001 to 2010 and 3.21% from 2010 to 2020.

To emphasize the different dynamics during different periods, we summarize the results in Table II.1. While the great recession, which hit the lower end of the market especially hard, can explain part of the dynamics, it cannot explain all of it. Even during the booming years before the great recession, the growth rates at the lower end were still much lower than the growth rates during the 2010-2020 period.

It is clear from these two graphs that it is difficult, if not impossible, for a potential homebuyer to purchase a home for less than $150,000 in the current market. These are profound changes in the pricing of the Charlotte region’s home market and have significant implications for the ability of first-time homebuyers to enter the market. Before 2011, it was reasonable for a household transitioning into first-time homeownership to assume they would be able to find a home priced under $150,000—the typical “starter home” price. By 2020, however, that same household would realistically need to assume that starter homes are those with prices of $300,000 or less. As we will show later in this section, this directly affects the affordability of housing and the ability of people to transition to housing.

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Figure II.14 Dynamics of the House Price Distribution 2001-2020

Table II.1 Annual Growth along the Housing Price Distribution 10th Percentile 25th Percentile Median 75th Percentile 90th Percentile

2001-2010 -5.04% -1.11% 1.30% 3.11% 3.74%

2010-2020 12.32% 8.37% 6.39% 4.31% 3.21%

We seek to illustrate the affordability challenges in two ways. First, we will examine how much income it would take to qualify for a mortgage each year for percentages of the housing sales distribution. That is, for each year we will show the amount of income that one would need to be able to purchase the 10th percentile home, the 25th percentile home, and the median-priced home.

We will be able to do this for each year of our study from 2005 until 2020. Second, we will flip this around, and for 2021 demonstrate the median income for various professions, the prices of the homes that could be purchased.

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Following the commonly used criteria for affordable housing, we consider housing is affordable if the total housing-related cost is below 30% of total gross income. We include the following items in housing-related costs: mortgage payments, property taxes, property insurance, mortgage insurance, and utilities. We also make the following assumptions. First, the homeowner uses a 30-year, 95% loan-to-value (LTV) fixed-rate mortgage at the prevailing rate; second, we assume that the property tax rate is 1.05%; third, private mortgage insurance is 0.5% of the initial mortgage balance; finally, we rely on the census data for typical utilities and property insurance expenditure.4 Table III.2. Income Required to Afford Values Percentiles of the Housing Distribution by Year. Home prices for various percentiles are presented in Table III.5. Mortgage rates are from the St. Louis Federal Reserve site, Median Utilities and property insurance are as reported by the U.S. Census Bureau for the southern United States in 2013, inflated or deflated by inflation as reported by the U.S. Bureau of Labor Statistics. Homeowners are assumed to use a 30year, 95% mortgage. Property taxes are assumed to be 1.05% annual, and PMI is assumed to be 0.5% of the initial mortgage balance.

Year 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

10th Percentile Price $32,244 $34,146 $34,198 $30,987 $25,545 $23,855 $22,550 $23,369 $27,351 $29,652 $32,288 $34,708 $39,644 $45,138 $46,778 $50,617

25th Percentile Price $42,300 $45,264 $46,253 $43,448 $38,205 $35,992 $34,299 $34,838 $39,521 $41,923 $43,508 $45,605 $50,781 $57,351 $59,205 $62,860

Median Price $55,110 $59,529 $62,327 $59,094 $51,888 $51,241 $50,166 $49,680 $54,415 $57,499 $59,743 $62,230 $68,996 $75,917 $75,668 $79,014

4

We obtained the annual average mortgage rate for each year as reported by the Federal Reserve at: https://fred.stlouisfed.org/series/MORTGAGE30US. We used the median gas, electric, and water bills for the southern United States as reported as reported by the U.S. Census Bureau American Fact Finder web-site: https://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml. The Census Bureau only reports this for 2013. We deflated or inflated the value based on the Bureau of Labor Statistics inflation calculator (https://www.bls.gov/bls/inflation.htm.) For property taxes we assumed a rate of 1.05% of market value. We assumed a 0.5% monthly charge for PMI.

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Table II.2 shows several facts about affordability in the Charlotte region’s owner-occupied housing market. First, although the income required to buy housing has increased at all points along the housing distribution, the lowest end of the distribution has been affected the most. In 2005, the minimum income to affordably buy the 10th percentile home was $32,244. In 2020, the income required to buy the 10th percentile home was $50,617, a 57% increase. Even if we inflate the 2005 income into 2020 dollars, the 2005 income would be equivalent to $42,755. This means the 10th percentile price has risen at a greater rate than inflation. It is even more daunting if one looks at the prices available at the height of the recession. In 2010, the 10th percentile home could have been purchased with an income of $23,855. In the ten years since 2010, the cost has more than doubled. In nominal terms, the income required to buy the 10th percentile home has risen by 112%, and in real terms, it has increased by 79%.

As one moves to higher-priced homes, these effects, while still present, become less pronounced. For example, the income required to purchase the 25th percentile home was $42,300 in 2005, $35,992 in 2010, and $62,860 in 2020, or increases of nominal required incomes of 48% and 75%, respectively. In inflation-adjusted real terms, they represent increases in the required income of only about 12% since 2005 and 47% since 2011. For the median-priced home, the required income has changed from $55,110 in 2005, to $51,241 in 2010, to $79,014 in 2019.5 In nominal terms, this is an increase of 43% since 2005 and 54% since 2010. In real terms, it is an 8% increase since 2005 and a 30% increase since 2010.

The largest increases in required income to obtain a home, whether one uses 2005 or 2010 as the base year, occurred in the lowest portion of the housing distribution. The price increases were more moderate in the middle (and upper) portions of the housing price distributions. The reduction in mortgage interest rates from 2005 to 2020 also decreases the required incomes. Because the balances (and hence interest payments) on higher-cost homes tend to be higher, the lower interest rates had a more ameliorating effect on the incomes required at the higher ends of the price spectrum.

Tables II.1 and II.2 are instructive because they show how the combination of changing home prices, changing interest rates, and inflation have affected housing affordability. Most people, of 5

For both the median-priced home and the average-priced home, the year with the smallest minimum required income was 2012. We are using 2011 here for consistency with the 10th and 25th percentile-home analysis.

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course, do not really ask the question, “If I want to buy house X, what is the minimum income I need to have?” Most of us tend to think of this the other way around, “If I have an income of Y, what is the upper price limit of the house that I can afford?” We address the question this way in Table III.3.

In Table II.3, we look at the 2019 median income in North Carolina for a variety of jobs and answer the question, “Given this level of income, how expensive of a home could this person have bought and still not have spent more than 30% of their income on housing?” The median income data are from the North Carolina Star Jobs database (www.nccareers.org/starjobs/star_jobs.html).

The median incomes reported are for the entire state, not just the Charlotte region.

Table II.3 illustrates that until one’s income reaches between $50,000 and $60,000 per year, it is difficult to find significant numbers of homes that he or she can afford. The table shows that many occupations, generally considered to be permanent and career-professions, such as firefighters, police officers, and elementary school teachers, do not have median incomes at these levels.6 We also want to emphasize that affordability has improved compared with 2019 largely because of the lower mortgage interest rate, which also partly explains the strong housing demand despite the rapidly increasing housing prices.

6

Again, we emphasize that these median wage levels are at the state level. Actual wages in a metropolitan area such as Charlotte may be higher.

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Table II.3. 2019 Median Wages for Various Professions, Home Prices They Can Afford, and the Percentage of Houses Selling at or Below That Price. The affordable home price is defined as the maximum home price that the homeowner could buy such that their total housing expense would not exceed 30% of their gross income. Total housing cost includes the monthly payment on a 30-year, 95% LTV mortgage at an interest rate of 3.11%, monthly utilities and property insurance costs of $345, property taxes of 1.05%, and private mortgage insurance of 0.5% of the original loan amount. Utility and property insurance comes from the U.S. Census Bureau American Fact Finder website https://factfinder.census.gov, and is inflated from their 2013 estimate. Inflation information from the U.S. Bureau of Labor Statistics, https://www.bls.gov/bls/inflation.htm. Mortgage rate data is from the St. Louis Federal Reserve (https://fred.stlouisfed.org/series/MORTGAGE30US.)

Median Wage

Affordable House

Percentage Sold in Charlotte Region

Percentage Sold in Mecklenburg County

$21,000 $21,000 $35,440 $31,240 $32,590 $37,260 $45,210 $45,860 $53,600 $53,650 $62,940 $68,520 $69,840 $72,030 $78,200 $80,480 $90,550 $91,550 $97,290 $100,710 $109,710 $111,730 $127,250

$49,371 $49,371 $148,389 $119,589 $128,846 $160,869 $215,383 $219,840 $272,914 $273,257 $336,960 $375,223 $384,274 $399,291 $441,600 $457,234 $526,286 $533,143 $572,503 $595,954 $657,669 $671,520 $777,943

0.25% 0.25% 4.01% 2.20% 2.74% 5.27% 13.00% 13.35% 26.75% 26.83% 45.96% 57.25% 59.12% 62.68% 70.97% 73.36% 80.93% 79.79% 84.32% 85.78% 88.92% 89.49% 92.70%

0.00% 0.00% 0.71% 0.29% 0.38% 1.37% 5.24% 5.51% 18.64% 18.71% 39.90% 51.39% 53.03% 56.61% 65.05% 67.45% 75.49% 75.98% 79.41% 81.11% 84.66% 85.31% 89.50%

Profession

Hotel Desk Clerks, bartenders, maids Hairdressers, Hairstylists, and Cosmetologists Ophthalmic Medical Technicians Tellers Fire Fighter Meter Readers, Utilities Graphic Designer Elementary School Teachers Healthcare Social Workers Librarian Registered Nurses Web Developers Accountants and Auditors Genetic Counselors Architects, Except Landscape and Naval Civil Engineers Sales Engineers Veterinarians Construction Managers Air Traffic Controller Purchasing Managers Training and Development managers Pharmacists

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B.

Other Owner-Occupied Markets Besides single-family houses, there are also other types of owner-occupied houses, such as

condos, townhomes, and apartments in the market. We therefore conduct a similar analysis as the single-family market to gain more insights into this market. We first plot the median and average prices for the region and Mecklenburg County in Figure III.16. The prices of other owner-occupied houses are lower than those of single-family houses. In January 2020, the median price of other owner-occupied houses was $222,000, while the median price of single-family houses was 273,500. However, the price dynamics are remarkably similar. The growth rate of the median price from September 2020 to September 2021 is 16.4%, which is very close to the 16.3% growth rate of the single-family median house over the same period. The other interesting thing to look at is the composition of the owner-occupied housing markets, which could gauge whether COVID has changed the preference for housing. To this end, the percentage of the sales of other owneroccupied housing has not changed much from January 2020 (16.3%) to September 2021 (16.6%).

Figure II.15 Prices of Other Owner-Occupied Houses January 2020-September 2021

Next, we also try to gain more insights into the supply side of this market. We again first compare the listings and sales in Figure III.17. While this market is also pretty tight, it is not nearly The State of Housing in Charlotte Report 2021

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as bad as the single-family market. For most months, the number of listings was higher than the number of sales. We then continue to assess the tightness of this market by looking at the median days on the market (Figure III.18) and the percent of homes sold above listing prices (Figure III.19). Both figures are remarkably similar to that of the single-family market. In particular, the median days on the market also hit a low of three days in May 2021, and the percent of homes sold above listing prices hit an astonishing 50% in June 2021. All these show that the COVID-19 pandemic has impacted all segments of the own-occupied home market similarly, fueled by strong demand and an extremely limited supply.

Figure II.16 Listings and Sales of other Owner-Occupied Houses January 2020-September 2021

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Figure II.17 Median Days on the Market of other Owner-Occupied Houses January 2020-September 2021

Figure II.18 Percent of Other Owner-Occupied Homes Sold Above Listing Prices January 2020-September 2021

The owner-occupied housing market is by far the largest component of our overall housing market. Typically, 65% or more of households in the region live in a housing unit that they own.

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Because of its scale, the owner-occupied market is what most affects the housing options available to all people in the region.

C.

Difficulties of homeowners during the pandemic Finally, to better understand the impact of the COVID-19 pandemic on homeowners, we use

the Household Pulse Survey conducted by the U.S. Census Bureau to examine the homeowner difficulties. The household pulse survey is a bi-weekly survey designed by the Census Bureau to quickly deploy data collected on how people’s lives have been impacted by the COVID-19 pandemic. Starting from Phase 2 of the survey (August 19-31), the survey started to ask survey respondents whether they are behind on their mortgages. We therefore use the survey data to calculate the percentage of households that are behind on their mortgages. One thing to note is that the smallest geographical area collected by the survey is a state. The results we present here are hence for the whole state of North Carolina, instead of just the Charlotte region.

Figure II.19 Percent of Households behind on Mortgage Payments during the Pandemic

We first calculate the percent of all homeowners who are behind on mortgage payment in Figure II.19. We can see that in November 2020, more than 10% of all homeowners were behind The State of Housing in Charlotte Report 2021

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on their mortgage payments. However, the number has since decreased substantially. By the end of September 2021, only about 2-3% of all homeowners are behind on their mortgage payments.

The impact of COVID-19 is certainly not even. We therefore also show the percent of white, African American, and Hispanic households that are behind on their mortgage payments also in Figure II.19. African American and Hispanic households suffered much more during the pandemic. In particular, the percent of African American households behind on mortgage payments reached an astonishing 28% in November 2020. And the number was still close to 10% by the end of September 2021. Similarly, a much higher percentage of Hispanic households were also behind on their mortgage payments, reaching 23% in February 2021 and remaining at a high percentage till today.

Figure II.20 Percent of Households behind on Mortgage Payments during the Pandemic

by Income Levels

In addition to race and ethnicity, we also examine how the pandemic impacted households with different levels of income differently. The Household Pulse Survey categorize income into less than $25,000, $25,000-$34,999, $35,000-$49,000, $50,000-$74,999, $75,000-$99,999, $100,000-$149,999, $150,000-$199,999, and greater than $200,000. We therefore calculate the percentage of households that are behind on mortgage payments for each income category, as The State of Housing in Charlotte Report 2021

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shown in Figure II.20. The pandemic obviously hit low-income homeowners much more severely than high-income homeowners. In November 2020, more than 60% of homeowners with an annual income less than $25,000 were behind on their mortgages, and more than 30% of homeowners with an annual income between $25,000 and $34,999 were behind. On the other hand, the impact of the pandemic on homeowners earning more than $100,000 is minimal.

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III. Rental Housing Market Traditionally, the rental housing market is dominated by multi-family apartments. However, in recent years, many single-family houses also entered the rental market. We, therefore, will provide analysis both for the multi-family apartment market and the single-family rental market.

A.

The Multi-Family Apartment Rental Market We start our analysis of the rental market with the multi-family apartment market. Before we

do, however, it is important to address data and data limitations. Commercial data suppliers such as CoStar provide significant data on rents, occupancies, and other characteristics, and for the analysis below we rely primarily on the CoStar data. CoStar collects data on virtually the entire universe of large-scale complexes, typically those with 40 or more units. They also provide data on some smaller-scale complexes, but do not have the same universality of coverage. Therefore, the smaller complexes are underrepresented in this data. It is also important to note that the CoStar data changes over time, as properties with historical operations can be added to the CoStar database, they can be redeveloped (which would change property quality), or properties may simply stop providing data. Accordingly, all data (historical and current) is updated in this report with the latest CoStar data, which may not correspond exactly to the data presented in prior versions of this report. Furthermore, all references to “2021 Q4” and “2021 YTD” are data reported in CoStar as of mid-October.

The apartment market has grown dramatically in the Charlotte region. Figure III.1 shows the number of units tracked by CoStar each quarter from 2000 to 2021. Over this entire time frame, the region has added 114,348 units or an increase of 104.5%. The growth rate since 2010 has been particularly noticeable, with 74,728 units added. This is an increase of 54.5% or an annualized growth rate of 4.0%. The majority of apartment units in the region are located in Mecklenburg County. Figure III.2 shows the distributions of apartment units by county. While there are significant numbers of apartment units in Cabarrus, Gaston, Iredell, and York counties, approximately 73% of all apartment units in the region are located in Mecklenburg County.

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Figure III.1 Number of Apartment Units in the Charlotte Region 2000-2021

Figure III.2 Distribution of Apartment Units by County 2021

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It is also useful to understand how the apartment market is divided along different dimensions.

In Figure III.3 we present the breakdown of units by the number of bedrooms in the unit. We categorize them as being studio, one bedroom, two bedroom, three bedroom, and more than three bedroom. By far, the one and two bedroom units are the most common, constituting 84% of the entire apartment stock.

To understand the relative quality of the region’s apartment stock, Figure III.4 presents the distribution of apartments by CoStar’s quality rankings. Each apartment complex is rated as being “A”, “B”, “C,” or other quality.7 The “A” quality apartments are typically new construction with high amenity levels and prime locations, while the “B” and “C” units tend to be relatively older, have fewer amenities, and in less desirable locations.

Figure III.3 Charlotte Region Distribution of Apartment by Size 2021

7

CoStar uses an F ranking which we categorize as “other.”

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Figure III.4 Charlotte Region Distribution of Apartments by Quality

Looking at the rental rates for apartments over time is also instructive. Again, we use data provided by CoStar. In Figure III.5 we plot the average effective rent per unit over time. The effective rent is the net rent paid by the tenant once any concessions, such as a month’s free rent, are taken into account. Figure III.5 presents the data in two formats, the average effective rent per unit on the left side vertical axis, and the average rent per square foot on the right vertical axis.

While these show the same basic trend, the per square foot measure does provide the clearest measure since it does take into account the differences in unit size.

The data in Figure III.5 show that rents have increased in the region over time, with the sharpest rent increases occurring since 2010. In that time, the average rent has increased, on a per-unit basis, from $898 to $1,390, a 54.8% increase. This is an annualized rate of growth of 4.1%. On a square foot basis, the average rent has increased from $0.94 per square foot to $1.46 per square foot, a 55.3% increase or a 4.1% growth rate.

Remarkably, approximately one-third of the growth in rents since 2010 occurred in just one year. In 2021, average effective rents increased by $198 per unit (or 16.6%) from the prior year.

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Examining the data closer, the quarterly growth rate in 2021 was 3.6% (Q1), 6.8% (Q2), and 5.3% (q3). Accordingly, there are signs that the growth is decelerating, as rents peaked in the second quarter and growth has been decelerating to the fourth quarter.

Figure III.5 Charlotte Region Effective Rent for Apartment 2000-2021

It is worth noting that the increase in rental rates is broadly consistent with changes in singlefamily homes in the owner-occupied market, in that the largest increases have happened in the lower-priced segment of the market. To see this, consider Table III.1, presented below. In this table, we compare changes in the asking rents of both “A”, “B”, and “C” apartments in the region since 2010 along with the change in the average home price in the region. During this time period, the average “A” rent has increased by 43.4%, or at an annual rate of 3.3%. The average “B” rent has increased by 59.2%, or 4.3% per year. Finally, the average “C” rent has the highest growth rates as rents have increased by 69.5%, or 4.9% per year.8

8

We note that the average rent growth in the entire market (Figure III.39) was approximately the same as the average rent growth for the “B” quality apartments during 2010-2021. This is a result of the market being dominated by more “B” units (52% of supply) relative to “A” and “C” units.

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Table III.1. Asking Rents for “A”, “B”, and “C” Apartments and Average Home Price for the Charlotte Region by Year, and Annualized Rent/Price Growth. Rental data come from CoStar. Average home price is from CKCRE tabulations of Canopy Realtor © Association Multiple Listing Service data.

"A" Annual "B" Annual "C" Annual Average Annual Effective Percentage Effective Percentage Effective Percentage Home Percentage Rents Change Rents Change Rents Change Price Change

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

$1,169 $1,193 $1,228 $1,254 $1,271 $1,309 $1,322 $1,323 $1,378 $1,432 $1,406 $1,676

2.05% 2.93% 2.12% 1.36% 2.99% 0.99% 0.08% 4.16% 3.92% -1.82% 19.20%

$862 $875 $901 $927 $951 $1,001 $1,029 $1,051 $1,099 $1,149 $1,177 $1,372

1.51% 2.97% 2.89% 2.59% 5.26% 2.80% 2.14% 4.57% 4.55% 2.44% 16.57%

$613 $618 $638 $660 $689 $729 $772 $804 $844 $889 $933 $1,039

0.82% 3.24% 3.45% 4.39% 5.81% 5.90% 4.15% 4.98% 5.33% 4.95% 11.36%

$222,144 $214,326 $224,550 $238,840 $245,928 $257,569 $274,632 $298,182 $331,996 $334,628 $365,466 $420,537

-3.52% 4.77% 6.36% 2.97% 4.73% 6.62% 8.57% 11.34% 0.79% 9.22% 15.07%

These rental rates are presented in nominal dollars, of course. Since 2010, overall inflation has been relatively low by historical standards. From 2010 to 2021 the cumulative inflation rate was 24.7% (2.0% per year on average), meaning that from 2010 to 2021 the “A”, “B”, and “C” apartment rents, as well as the average home price, have all grown at a rate faster than inflation.9 In fact, since 2010 the real growth rate (i.e. inflation-adjusted growth rate) for “A”, “B”, and “C” apartment rents has been 1.3%, 2.3%, and 2.9%, respectively. The annual growth rate in the average owner-occupied home in the region over that same time period was 6.0%, or 3.9% when adjusted for inflation.

It is worth noting, however, that over a longer time frame the price of apartments in the Charlotte region has actually gotten cheaper in real terms. To see this, consider Figure III.6. Figure III.6 plots the average rent for “A”, “B”, and “C” apartment units since 2000. In 2000, the average “A” unit effective rent was $1,415. In 2021 dollars, this would be $2,230, and compared with the actual average “A” unit rent in 2021 of $1,676 means that the real rent decreased by 24.8% since 2000. Similar results hold for both “B” and “C” quality units. From the graph, it is clear, of course, that by 2002 nominal rents had fallen, and a similar analysis would show a small net positive real increase in rents since 2002.

Figure III.6 Charlotte Region Average Effective Rent for Apartments by Quality 9

From the Bureau of Labor Statistics web site: https://www.bls.gov/data/inflation_calculator.htm.

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2000-2021

We are also interested in understanding how apartment rental rates vary across the region.

Figure III.41 shows that Mecklenburg County has the highest average effective rent in the region, while Lincoln County has the lowest effective rent. Figure III.42 shows that Lancaster and Union counties have the highest vacancy rates, but in both cases, there are a significant number of newly opened complexes that are still in an initial “lease up” phase which temporarily inflate vacancy from their long run averages.

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Figure III.7 Effective Rent per Unit by County 2021

Figure III.8 Charlotte Region Apartment Vacancy Rate by County 2021

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B.

The Single-Family Rental Markets Next, we turn to the single-family rental markets. Because many of the single-family rental

properties are owned and managed by individual investors, it is difficult to get comprehensive data on the single-family rental markets. In this part of the analysis, we again rely on CanopyMLS data.

Some individual real estate investors use real estate agents to list their rental properties, and these listings are covered by the MLS. We want to emphasize that it is difficult to assess what percentage of single-family rental properties are listed on the MLS. But nonetheless, we can still gain some insights into the general trends of the market by just looking at the MLS data.

We first present the median and average single-family house rents during the COVID-19 pandemic in Figure III.9. Before the COVID-19 pandemic in January 2020, the median rent in the region was slightly below $1,500 per month. By September 2021, however, the median monthly rent has dramatically increased to almost $1,900, a 26.7% increase. Considering the annual change from September 2020 to September 2021, the annual growth rate was still 11%. These results again show that the demand for rental housing was also extremely strong during the pandemic.

Figure III.9 Median and Average Single-Family House Rent January 2020-September 2021

We then again take a long-term perspective and look at single-family rents from 2003 to 2020.

The Canopy MLS data started to cover rental listings in 2003, that is why we start the analysis The State of Housing in Charlotte Report 2021

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from 2003 as well. We plot the annual median and average rents in Figure III.10. From 2003 to 2020, the median single-family rent went from $1,000 per month to $1,595 per month, a 60% increase. The average annual growth rate is 2.37%, much lower than the 5%-6% single-family price growth date over the same period. From this, we can see that the COVID-19 pandemic has greatly sped up the rental growth rate in this region.

Figure III.10 Median and Average Single-Family House Rent 2003-2020

We then also try to understand whether there is also a shift in the distribution by looking at the 10th, 25th, 75th, and 90th percentile of the monthly rents. Similar to single-family house prices, single-family rents at the lower end also increase at a faster rate than rents at the higher end. From 2010 to 2020, the annual growth rate is about 5% for the 10th percentile, 4.4% for the 25th percentile, 2.8% for the 75% percentile, and 2.2% for the 90th percentile. However, one important thing to note is that this pattern almost completely reversed in 2020, during which the higher-end experienced a higher growth rate. We believe this is driven by the demand for larger space during the pandemic. Another way to look at this issue is to examine the rent growth rates for different sized single-family rentals, as shown in Figure III.12. While the overall growth rates on singlefamily rental with different numbers of bedrooms are similar, the growth rate for three- and fourThe State of Housing in Charlotte Report 2021

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bedroom rentals have much higher growth rate from 2019-2020, that is, during the COVID-19 pandemic.

Figure III.11 Dynamics of the Single-Family Rent Distribution 2003-2020

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Figure III.12 Single-Family House Rents by Size 2003-2020

Figure III.13 Single-Family Rental Listings and Closings January 2020-September 2021

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The results above clearly suggest that the demand for single-family rental housing is getting stronger. We next turn to the supply side by first looking at the number of listings and closings.

We first plot the monthly listings and closings during the COVID-19 pandemic from January 2020 to September 2021 in Figure III.13. Similar to single-family listings and sales, in most months during the COVID-19 pandemic, the number of closings is higher than the number of listings, suggesting that the supply of single-family rental is also extremely tight. We then also take a longer-term perspective and plot the listings and closings from 2003 to 2020 in Figure III.14.

Again, in most previous years, the number of listings was higher than the number of sales. Even among the very few years in which the number of listings was lower, the differences are much smaller than that of 2020.

Figure III.14 Single-Family Rental Listings and Closings 2003-2021

We then also look at the median days on the market for single-family rentals first during the pandemic, as shown in Figure III.15. Before the pandemic in January 2020, the median days on the market was 35 days. However, in June and July 2021, the median days on the market has dropped to seven days, suggesting that the single-family rental market also became extremely tight The State of Housing in Charlotte Report 2021

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during the pandemic. We then also put this into a long-term perspective, as shown in Figure III.15.

Again, the market has never been so tight until during the COVID-19 pandemic.

Figure III.15 Median Days on the Market for Single-Family Rentals January 2020-September 2021

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Figure III.16 Median Days on the Market for Single-Family Rentals 2003-2020

C.

The Subsidized and Public Rental Markets This section will demonstrate that there is a segment of the population who do not have the

income to purchase or rent housing at current market prices. This segment of the population relies upon either subsidized private housing or public housing to have a place to live. The need for subsidized or public housing is frequently thought of as an “urban” problem, but the need is throughout the entire region as we will show shortly.

We begin this analysis with a focus on housing affordability. More specifically, we compare the all-in cost of owning or renting to household income. Table III.2 reports that as of 2021 the average “C” grade apartment in the Charlotte area rents for $1,039/month. The U.S. Census Bureau reports average household utilities in the region in 2020 as being $367 (when adjusted for one year of inflation).10 This means the total housing cost for the household renting the average “C” unit would be $1,406/month. To meet the normal definition of being affordable, the homeowner must

10

Utilities rates are from the U.S. Census Bureau https://www.bls.gov/cex/tables/calendar-year/mean-item-shareaverage-standard-error/cu-region-1-year-average-2020.pdf for the Southern United States in 2020.

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be able to pay their rent and utilities with no more than 30% of their gross income. This implies a monthly household income of $4,686/month, or $56,230 annually for the average “C” level apartment to be considered affordable.

There are large segments of the regional population who cannot meet this financial hurdle.

Recall that our last report shows that for the entire eight-county Charlotte region in 2019, approximately 360,000 households (37% of all households) had incomes less than $50,000. Every one of those households would be considered cost-burdened renting the average “C” level apartment. Consider also that nearly 80,000 households (8.1% of all households) in the region have an annual household income of less than $15,000. Essentially, renting a “C” grade apartment and paying utilities would use up their entire income.

Therefore, there are a substantial number of households that need some type of housing assistance. But before beginning our discussion of subsidized or public housing, it is worth clarifying a few terms. Every year the U.S. Department of Housing and Urban Development (HUD) works in conjunction with the U.S. Census Bureau to determine the area median income (AMI) for a family of four in the region. When a household applies for housing assistance, the level of assistance for which they are eligible is a function of the number of people in the household and the household income relative to the AMI. Typically, families become eligible for some assistance at the 80% of AMI level, but the largest levels of assistance occur at the low-income (50% of AMI) and very low-income (30% of AMI) levels. Table III-9 reports the Charlotte region AMI and the 50% and 30% AMI levels for 2005 through 2021.

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Table III.2. Annual Incomes and Maximum Amount Household Can Devote to Housing Without Being Cost-Burdened for Various Percentages of the Charlotte AMI by Year 20052021. Data are from the U.S. Department of Housing and Urban Development’s website, http://huduser.gov.

100% of Charlotte Region AMI

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Annual Income $61,800 $64,400 $60,200 $64,300 $66,500 $67,200 $67,500 $68,500 $64,100 $64,200 $67,200 $67,000 $70,700 $74,100 $79,000 $83,500 $84,200

50% of Charlotte Region AMI

Monthly Housing $1,545 $1,610 $1,505 $1,608 $1,663 $1,680 $1,688 $1,713 $1,603 $1,605 $1,680 $1,675 $1,768 $1,853 $1,975 $2,088 $2,105

Annual Income $30,900 $32,200 $30,100 $32,150 $33,250 $33,600 $33,750 $34,250 $32,050 $32,100 $33,600 $33,500 $35,350 $37,050 $39,500 $41,750 $42,100

Monthly Housing $773 $805 $753 $804 $831 $840 $844 $856 $801 $803 $840 $838 $884 $926 $988 $1,044 $1,053

30% of Charlotte Region AMI Annual Income $18,540 $19,320 $18,060 $19,290 $19,950 $20,160 $20,250 $20,550 $19,230 $19,260 $20,160 $20,100 $21,210 $22,230 $23,700 $25,050 $25,260

Monthly Housing $464 $483 $452 $482 $499 $504 $506 $514 $481 $482 $504 $503 $530 $556 $593 $626 $632

Recall that the amounts in the “Monthly Housing” column include the actual housing payment and utilities. Therefore, a household at 100% of AMI for the Charlotte region could afford to pay $2,105 for rent and utilities in 2021. Again, assuming an average utility burden of $367, this means they could pay as much as $1,738 in rent. This places them well into the private rental market.

Unfortunately, utilities do not scale with rent or income, so a person at 50% of AMI would likely still spend around $367 for utilities. This leaves them with $686 with which to pay rent. This places them approximately 34% below the average rent for a “C” class apartment in the region. This will make it extremely difficult for them to find private apartments in the Charlotte region. For people at 30% of AMI, the $632/month they can devote toward all housing costs would be split into $367 toward utilities and $265/month toward rent. There are virtually no units available in the private marketplace at that price level. Households at the 50% and 30% levels will have to rely upon either subsidy to find private apartments or public housing.

Within the subsidized and public housing markets, there are three main programs: the Low Income Housing Tax Credit program (LIHTC), the Section 8 rental assistance program, and The State of Housing in Charlotte Report 2021

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publicly owned housing units. The following sections briefly discuss and analyze the first two of these programs within the Charlotte region.

The Low Income Housing Tax Credit is a Federal program designed to encourage private investment in affordable rental housing for low-income households. The tax credits are created through the IRS and allocated to state housing credit agencies based on the population of the state.

Project sponsors (i.e. developers) are given a tax credit in exchange for making a certain percentage of the units in the development available to low-income renters.11 These tax credits can be transferred to investors and are typically used to attract equity financing for the deal. These affordable units are rent-restricted in that the maximum amount of rent that can be charged is equal to 30% of the relevant AMI, less utilities. The rent restrictions are required to be kept in place for 30years (although owners can leave the program after the first 15 years if granted regulatory relief) and the tax credits can be recaptured if the restrictions are not honored during the first 15 years.

HUD is involved in determining the AMI on an annual basis and adjusting it based on the number of people in each household.

The LIHTC program has been used extensively throughout the country, including in the Charlotte region. Figure III.17 shows the distribution of LIHTC units by county.12 We note that while the vast majority of LIHTC units are in Mecklenburg County, some counties have a higher proportion of LIHTC units relative to Mecklenburg than they do of general apartments. For example, Gaston County has 1,857 LIHTC units, which is 18.2% of the 10,217 LIHTC units that Mecklenburg has. As shown in Figure III.36, CoStar reports that Gaston County has 13,193 total apartment units or only 8.5% of the 154,538 units that Mecklenburg has. A similar result holds in Cabarrus County. Figure III.18 shows the distribution of unit sizes across each county. This distribution is essentially the same as the distribution CoStar reports for non-subsidized units, although the subsidized market is skewed toward having more bedrooms, as it has 10.2% fewer one-bedroom units and 11.2% more three-bedroom units on average.

11

Sponsors must agree to devote, at a minimum, at least 40% of the units to people earning 60% of AMI or less, or 20% of their units to people earning 30% of AMI or less.

12 As of the date of this report, LIHTC data are available only for projects placed in service through 2019. Per the HUD website, data for properties placed in service during 2020 will be added to the database in the spring of 2022.

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The Section 8 program is another program that seeks to leverage the private market to provide affordable housing to low-income residents. Section 8 is a federal housing assistance program that provides direct assistance to certain low-income tenants. Although ultimately funded through HUD, Section 8 programs are administered at the local level by public housing authorities. The aid is given to the tenant in the form of a voucher which the landlord then redeems for cash. The amount of aid given is enough to allow the tenant to spend no more than 30% of their income on rent plus utilities. Vouchers may be based on the individual tenant, in which case their voucher follows them should they move. These are known as Housing Choice Vouchers. Alternatively, vouchers may be tied to the property and not to the tenant. This essentially means the voucher does not follow the tenant should they leave. These are referred to as project-based vouchers. HUD maintains a database of projects across the entire country that have contracts with HUD or with public housing authorities for project-based vouchers. Figure III.19 shows the distribution of those projects within the Charlotte region.

Figure III.17 Number of LIHTC Units by County 2019

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Figure III.18 Distribution of LIHTC Units by Size 2019

This distribution shows again that the need for low-income housing is spread throughout the region. Even though Mecklenburg County has the highest absolute number of these project-based voucher contracts, counties such as Cabarrus, Gaston, Iredell, and York have significant inventory as well.

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Figure III.19 Charlotte Region Count of Housing Choice Voucher Contracts by County 2021

D.

Difficulties of Renters during the Pandemic To better understand the difficulties renters experience during the COVID-19 pandemic, we

again use the Census Household Pulse Survey conducted during the pandemic. We again want to emphasize that the data presented below are for the state of North Carolina, instead of just for the Charlotte region. In particular, we look at the percent of renters that are behind on their rental payments, as shown in Figure III.20. For all renters, the percent behind on rental payments kept at an elevated level of more than 10% from the first survey till now. The percent reached the highest of more than 30% towards the end of July 2021, possibly because of the expiration of the Federal COVID-19 relief programs. We also plot the percent of renters behind on their rental payments for different races. Again, the numbers are the lowest for white renters. In contrast, African American and Hispanic renters suffered the most during the pandemic.

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Figure III.20 Percent of Renters behind on Rental Payments during the Pandemic

To gain more insights into the uneven nature of the impact of the COVID-19 pandemic on renters, we plot the percent of renters behind on their rental payments for renters at different income levels in Figure III.21. It is obvious that lower-income renters suffered more from the COVID-19 pandemic than higher-income renters, and are more likely to be behind on their rental payments.

In addition to asking survey respondents whether they are behind on rental payments, the Household Pulse Survey also asks whether they are expected to be evicted in the next two months.

The response can answer “Very likely”, “Somewhat likely”, “Not very likely”, and “Not at all likely”. We calculate the percent of renters answering “Very likely” as a proxy for the rate of eviction, and plot it in Figure III.22. Overall, the percent of renters facing evictions remained at a fairly high level during the pandemic, and reached around 8% in late March and early April 2021.

Not surprisingly, the eviction rates also differ significantly across different races. Both African American and Hispanic renters faced much higher eviction rates during the pandemic. Looking at renters at different income levels, as shown in Figure III.23, most renters facing evictions are in the low-income category.

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Figure III.21 Percent of Renters behind on Rental Payments by Income during the Pandemic

Figure III.22 Percent of Renters Likely to be Evicted during the Pandemic

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Figure III.23 Percent of Renters Likely to be Evicted by Income during the Pandemic

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IV. Conclusion and Next Steps As we stated at the beginning of this report, our goal with the State of Housing in Charlotte Report is to provide a comprehensive, data-driven analysis of the state of housing in Charlotte and the surrounding area. We hope that this report will be useful to policymakers, market participants, and residents of the region as they make decisions regarding the housing markets.

The fundamental conclusion of this report is that overall, the state of housing in Charlotte is good.

The region does a good job providing adequate, relatively inexpensive housing for the majority of its residents. That said, we document several challenges, which include: •

The COVID-19 pandemic has led to dramatic increases in house prices.

•

Housing affordability issues are emerging and could become much worse over time.

•

The supply of houses, both owner-occupied and rental housing, is extremely tight, much worse than any time in history.

•

The rental rates have also increased dramatically during the COVID-19 pandemic.

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Appendix In our previous reports (2019 and 2020), we also presented an analysis of macroeconomic conditions in the Charlotte region. We also compared the state of the housing market in Charlotte with that of other regional and national competitor MSAs. These analyses relied on the annual American Community Survey (ACS) one-year estimates. In our last report, we presented the findings based on the 2019 ACS. The 2020 ACS was scheduled to be released in October 2021, which we could then use for this report. However, the release of the 2020 ACS was delayed and the data was still not available at the time we were writing this report. As a result, we are unable to provide an update on these analyses. However, the information is still necessary for the readers to better understand the state of the housing market, we therefore provide in this appendix the analyses in our last report.

Appendix I. General Macroeconomic Overview As we did in our previous report, we begin the analysis of the Charlotte region’s housing market by first examining some broad macroeconomic trends that affect the housing market.

Housing is but one component of the larger economy, and it both affects and is affected by the larger economy. In this section, we focus on three main issues in the macroeconomy – the growth in the local population, the growth in the income in that population, and then the growth in the aggregate supply of housing units. In Sections III and IV of the report, we go on to show how these broad macroeconomic trends contribute to the micro-trends we observe, especially those related to housing prices.

A. Data Sources In this section of the report, we rely heavily on data from the U.S. Census Bureau’s American Community Survey (ACS). Specifically, we rely upon data collected and published as part of the American Community Survey (ACS). This data set contains a wide array of data on households, housing, and housing characteristics. The most recent data set available is the 2019 ACS “vintage”, which was published in September 2020 by the Census Bureau.

The Census Bureau conducts the ACS on an annual basis. They then publish various statistics using the data they have collected during one of three different time periods. The “one-year” estimates are based solely on data collected within a single calendar year. The 2019 one-year estimates are based solely on data collected by the Census Bureau between January 1, 2019 and The State of Housing in Charlotte Report 2021

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December 31, 2019. They also publish “three-year” “five-year” estimates. These estimates are based on data collected over 36- and 60-month periods, respectively. The 2018 5-year estimates are based on data collected from January 1, 2014 through December 31, 2018.13 In selecting between these three data sets, there are tradeoffs. The five-year estimates are based on the largest sample. This means that they are least affected by random variation in the sample, and have the smallest “standard error” of the estimates. For many projects, these would be the most appropriate to use. The drawback to the five-year estimates, however, is that it mixes data across years. The one-year estimates are based on a smaller sample, and so are subject to more random variation, but have the advantage of being composed solely of data from a given year.14 Since the main goal of this study is to examine the evolution of the Charlotte housing environment over time, we have elected to use the ACS one-year estimates in the analysis, unless otherwise noted.

B. Population As illustrated in Figure AI.1, the population of the Charlotte region has grown significantly since 2010, although the rate of growth declined slightly in 2018 and 2019. Since 2010, the total population of the region has grown from 2,052,515 people to 2,438,105. This growth of 385,590 people is an increase of 16.7%, or an annualized growth rate of 1.95%. The growth rate in 2019 was 1.77%, which, while lower than the average over the nine years, was comparable to the growth rates in 2012 and 2013.

13

The Census Bureau has an in-depth discussion of these estimates at https://census.gov/programssurveys/acs/guidance/estimates.html. The 5-year estimates for 2019 were not yet available at the time of writing the report.

14 The one-year and five-year estimates are both statistically unbiased estimates – meaning that “on average” they will yield the same result. The smaller sample size of the one-year estimates just means that they are more likely to be affected by random variation than the five-year estimates.

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Figure AI.1. Charlotte Region Population and Population Growth Rates 2010-2019 2,500,000

3.00%

2,400,000

2.50%

2,300,000

2.07% 1.87%

2,200,000

1.68%

2.06%

2.10%

2.18% 2.00% 1.84%

1.82%

1.77% 1.50%

2,100,000 2,000,000

1.00%

0.99%

0.50%

1,900,000 1,800,000

0.00% 2010

2011

2012

2013

2014

Population

2015

2016

2017

2018

2019

Growth Rate

Source: US Census Bureau American Community Survey 1 year estimates.

The total population of the region has historically been split nearly evenly between Mecklenburg County and the surrounding suburban counties. In 2019, Mecklenburg County had an estimated population of 1,110,356 people while the suburban counties had a cumulative estimated population of 1,327,749 people, meaning slightly more than 54% of the region’s population was in the suburban counties and a little less than 46% was in Mecklenburg. The suburban counties had a population growth rate in 2018 of 2.06%, while Mecklenburg’s was 1.59%. In 2019, the growth rates are 1.99% for the suburban counties and 1.50% for Mecklenburg County.

As we noted in last year’s report, a consistent theme throughout our analysis is that suburban counties are, collectively, slightly larger than Mecklenburg. This is true for a variety of measures including population, housing units, and migration. We emphasize that this presents a challenge to the region: the fact that half of the area’s population is diffused across seven counties while the other half is concentrated in a single county means that what should be thought of as regional issues can at times be seen as either solely a Mecklenburg issue or solely as a suburban issue.

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Almost all of the challenges discussed in this report are most accurately viewed as regional issues that will take regional coordination across the eight counties to address.

Returning to the population statistics, as shown in Figure AI.1, the region has seen a population increase of 385,590 people since 2010. Mecklenburg County has had a population growth of 208,957 people and the suburban counties have had a population growth of 196,717. It is worth noting that the relative growth rates of Mecklenburg and the suburban counties have changed substantially over time.

Figure AI.2 shows this change in growth patterns. From 2011 through 2013, the population growth in Mecklenburg County exceeded that of the suburban counties. Since 2014, however, the suburban counties have had population growth greater than that of Mecklenburg County. Further, the differential in growth is getting larger over time. In 2014, the difference was only about 1,766 people, but in 2019, the differential was 9,495 people.

The annual growth rate for the region has increased from 1% in 2010 to 1.77% in 2019. Of that total growth, 48.5% has been in Mecklenburg County and 51.5% in the suburban counties. We note that most of the suburban growth has been concentrated in three counties, namely Cabarrus, Union, and York.

Figure AI.3 shows the distribution of the sources of population growth. As we noted in last year’s report, there are three sources for population growth. The first is the “natural increase” in population. This is simply the number of live births in the region less the number of deaths of residents. The second source is international migration. This includes both documented and undocumented immigrants. Finally, the third source of population increase is domestic migration.

This is defined to be migration from any place within the United States. Note that this includes migration from one county in the region to another. Note, however, that such intra-region migration does not cause any problems with our analysis because the exit of a resident from one county is offset by their entry into the other county.

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Figure AI.2 Population Growth 2010-2019 Mecklenburg and Suburban Counties 30,000 25,000 20,000 15,000 10,000 5,000 2010

2011

2012

2013

2014

Mecklenburg County

2015

2016

2017

2018

2019

Suburban Counties

The major sources of population growth in the region are domestic migration (60%), natural increases (28%), and international migration (12%). The percentages of domestic migration increased from 58% in 2017 to 60% in 2019. Figure AI.4 breaks this down into Mecklenburg and the suburban counties. As Figure AI.4 makes clear, there are stark contrasts in their respective sources of growth. The overwhelming majority of growth in the suburban counts (79%) comes from domestic migration.15 They have relatively little international migration and only moderate natural growth. Mecklenburg County, in contrast, has a relatively balanced mix with natural growth and domestic migration at 42% and 36%, respectively. Mecklenburg also has a significant international migration component at 22%.

Looking at the total population is interesting, of course, and gives a broad sense of what is happening in the housing market. What is at least equally important, however, is the rate of household formation. If the size of households is, on average, changing, the region might need to adjust its housing stock at a rate greater or less than the pure population growth. Figure AI.5 shows

15

We note that this would include migration from Mecklenburg County into one of the surrounding counties.

Similarly, the domestic migration component of Mecklenburg County would include people moving there from the suburban counties.

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the average household size in the Charlotte region over time. From 2010 to 2019, the average household size has been very steady at approximately 2.66 people per household.

Figure AI.3. Charlotte Region: Sources of Population Growth 2010-2019

Natural Increase 28% Domestic Migration 60%

International Migration 12%

Source: US Census Bureau American Community Survey 1 year estimates.

Figures II.6A and II.6B examine the growth in the number of households in the region.

Specifically, Figure AI.6A shows the total number of households in the Charlotte region, while Figure AI.6B shows the year over year growth in the region. Since 2010, the region has added 135,155 households. For understanding what is happening with the region’s housing markets, this figure is critical because it tells us the minimum number of housing units that must be built to satisfy the region’s growth.

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Natural Increase, 42%

Domestic Migration, 36%

International Migration, 22%

3.000

Figure AI.5. Charlotte Region: Average Household Size 2010-2019

2.900 2.800 2.700 2.600 2.500 2.400 2.300 2.200 2.100 2.000 2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

Source: CKCRE Tabulations of US Census Bureau American Community Survey 1 year estimates.

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Figure AI.6A. Charlotte Region Total Households 2010-2019

950,000

900,000

850,000

800,000

750,000

700,000 2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

Source: US Census Bureau American Community Survey 1 year estimates.

Figure AI.6B. Charlotte Region Annual Houshold Growth 2011-2019

30,000

25,000

20,000

15,000

10,000

5,000

2011

2012

2013

2014

2015

2016

2017

2018

2019

Source: US Census Bureau American Community Survey 1 year estimates.

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C. Income The Charlotte region has had significant economic growth and incomes have grown, both in real and nominal terms since 2010. As illustrated in Figure AI.7, in 2019, the mean income in the region was $92,376, an increase of 36.31% since 2010. In real terms, the 2019 mean income, expressed in 2010 dollars, was $80,912, and the real cumulative growth rate was 17.96%. The average annual nominal growth rate was 3.95% and the average annual real growth rate was 2.24%.

Figure AI.7. Charlotte Region Nominal and Real Mean Income 2010-2019 95000 90000 85000 80000 75000 70000 65000 60000 2010

2011

2012

2013

2014

Nominal Mean Income

2015

2016

2017

2018

2019

Real Mean Income

Source: CKCRE Tabulations of US Census Bureau American Community Survey 1 year estimates. Inflation data from Bureau of Labor Statistics

Given the diversity in the economic development of the counties across the Charlotte region, it is not surprising that there are variations in incomes and income growth rates. As illustrated in Figure AI.8, the top three counties with the highest 2019 household incomes, measured either in mean or median incomes, are (descending order) Union, Mecklenburg, and Cabarrus counties.

As shown in Figure AI.9, the income growth rate has varied across the region, with Lancaster County having the highest growth rate over that time frame. Lancaster started the decade with a relatively low-income level and has essentially caught up to York, Lincoln, and Iredell counties, as shown in Figure AI.8. Gaston still lags the region with an average income of slightly under $73,164, approximately $9,400 less than any of the other counties.

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$120,000

Figure AI.8. Charlotte Region: Mean and Median Incomes

by County 2019

$100,000 $80,000 $60,000 $40,000 $20,000 $Cabarrus

Gaston

Iredell

Lincoln Mean

Meckleburg

Union

Lancaster

York

Median

Figure AI.9. Annualized Nominal Growth Rate in Mean and Median Incomes by County 2010-2019 0.09 0.08 0.07 0.06 0.05 0.04 0.03 0.02 0.01 0 Cabarrus

Gaston

Iredell

Lincoln

Mean Growth Rate

Meckleburg

Union

Lancaster

York

Median Growth Rate

While examining mean and median incomes is useful, it can also be informative to look at the evolution of the entire income distribution over time. Figure AI.10 shows how the distribution of nominal household income has changed since 2010. In broad terms, it is relatively easy to see that incomes have generally risen and the number of households with incomes above $100,000 per year

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has risen the most. It is worth noting, however, that there are at least 135,722 households with an annual income of $25,000 or less.

Figure AI.10. Charlotte Region: Distribution of Houeshold Incomes 2010 and 2019 180000 160000 140000 120000 100000 80000 60000 40000 20000 0 Less than $10,000 to $15,000 to $25,000 to $35,000 to $50,000 to $75,000 to $100,000 $150,000 $200,000 $10,000 $14,999 $24,999 $34,999 $49,999 $74,999 $99,999 to to or more $149,999 $199,999 2010

2019

D. Housing Units Figure AI.11 shows that there has been significant growth in the number of housing units in the region. From 2010 to 2019, the number of housing units has increased by 132,512 units from 863,167 to 995,679 units. This works out to an annualized increase of 1.60%. Mirroring the overall population trends, there are more housing units in the suburban counties than in Mecklenburg County. There has been slightly more new unit construction in Mecklenburg County than in the suburban counties (67,784 in Mecklenburg vs. 64,728 in the suburban counties.) Figure AI.12 shows the distribution of housing units by county in 2010 and 2019.

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Figure AI.11. Housing Units in Charlotte Region 2010-2019 1,100,000 1,000,000 900,000 800,000 700,000 600,000 500,000 2010

2011

2012

2013

2014

2015

2016

2017

Source: US Census Bureau American Community Survey 1 year estimates.

Figure AI.12. Charlotte Region: Total Housing Units by County 2010 and 2019 500,000 450,000 400,000 350,000 300,000 250,000 200,000 150,000 100,000 50,000 0 Cabarrus

Gaston

Iredell

Lincoln Mecklenburg 2010

Union

Lancaster

York

2019

Source: US Census Bureau American Community Survey 1 year estimates.

The balance between the growth in population and the growth in housing units greatly influences the price of housing. In general, if the supply of housing does not increase by at least the rate of household formation, housing becomes relatively scarce and that will tend to increase The State of Housing in Charlotte Report 2021

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prices. As we noted last year, this also contributes to other problems, such as the gentrification of traditionally lower-income neighborhoods. Essentially, if wealthier households cannot find housing at a price point appropriate for their income level, they can “buy down” into less expensive housing and renovate. For lower-income households, however, this is usually not a practical strategy. The net effect is that a lack of supply in higher-priced housing propagates into lowerpriced housing and that results in sharply increasing prices at the lower end of the price spectrum.

As we noted last year, one implication of this is that when communities build higher-cost housing, it can potentially relieve gentrification pressure in lower-cost areas.

To explore this issue, we return to the relative changes in population and housing units in the region over time to assess the degree to which the region is underbuilding relative to the population growth. Figure AI.13 shows the percentage change in population for each county in the region and the percentage change in the number of housing units in those counties. In every county except Lincoln County, the population has grown at a faster rate over the 2010-19 period than the housing stock. In Cabarrus and Lancaster counties, the population growth rate has been seven percentage points higher than the housing unit growth rate.

Figure AI.13. Charlotte Region Cumulative Percentage Population and Percentage Housing Growth 2010-2019 30.00% 25.00% 20.00% 15.00% 10.00% 5.00% 0.00% Cabarrus

Gaston

Iredell

Lincoln

Cumulative Population Growth

Mecklenburg

Union

Lancaster

York

Cumulative Housing Unit Growth

Source: CKCRE Tabulations of US Census Bureau American Community Survey 1 year estimates.

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Figure AI.14 consolidates the population growth into a regional picture. Over the 2010 to 2019 period, the population grew at an annual rate of 1.93% and the number of housing units grew at a rate of 1.60%. We do note that in 2019, however, the housing unit growth rate was 2.37%, which was larger than the population growth rate of 1.77%.

Figure AI.14. Charlotte Region Population and Housing Unit Growth 2010-2019 2,500,000

2,000,000

1,500,000

1,000,000

500,000

0 2010

2011

2012

2013

Total Housing Units

2014

2015

2016

2017

2018

2019

Population

Source: CKCRE Tabulations of US Census Bureau American Community Survey 1 year estimates.

Although it is interesting to examine the total population relative to total housing units, it is perhaps more instructive to examine the growth in households to the growth in housing units.

Figure AI.15 shows the net increase in total households for the region and the net increase in housing units in the region from 2010-2019. Every year before 2018, the number of housing units added was fewer than the increase in the number of households. This means that the region was underbuilt in those years. In 2018, the number of new housing units was greater than the number of new households, but that was primarily because the number of new households dropped relative to 2017. The number of new housing units also dropped in 2018, but just by a smaller amount.

And the trend continued in 2019.

Indeed, if we examine the cumulative growth in both households and housing units over time, as Figure AI.16 does, it becomes very clear that the region has underbuilt over the past nine years.

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The good news is that the growth in the number of housing units outpaced the growth in the number of households in 2018 and 2019. In Figure AI.17, we highlight the cumulative difference between the number of new households and the number of new housing units.

Figure AI.15. Annual Change in Households and Housing Units for Charlotte Region 2010-2019 30000 25000 20000 15000 10000 5000 0 2011

2012

2013

2014

Household Change

2015

2016

2017

2018

2019

Housing Unit Change

Source: CKCRE Tabulations of US Census Bureau American Community Survey 1 year estimates.

Figure AI.16. Cumulative Change in Households and Housing Units in Charlotte Region 2010-2019.

140000 120000 100000 80000 60000 40000 20000 0 2011

2012

2013

2014

Cumulative Household Growth

2015

2016

2017

2018

2019

Cumulative Housing Unit Growth

Source: CKCRE Tabulations of US Census Bureau American Community Survey 1 year estimates.

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Figure AI.17. Cumulative Housing Unit Underbuilding in Charlotte Region 2010-2019.

2011

2012

2013

2014

2015

2016

2017

2018

2019

0 -5000 -10000 -15000 -20000 -25000

Source: CKCRE Tabulations of US Census Bureau American Community Survey 1 year estimates.

This, of course, begs the question as to how the additional population was housed. The answer is that there was vacant housing in the region that has now been taken up. As shown in Figure AI.18, there were approximately 93,179 vacant housing units in the Charlotte region in 2010, with 42,544 of those vacant units in Mecklenburg County. Since then, the number of vacant units has dropped to 90,526 in 2019.

The good news is that, in 2019, the number of new housing units did increase more than the population, resulting in a small increase in the vacancy rate in the region. As we reported last year, the vacancy rate in the region was very low at just 7.5%, and that increased slightly to 8.3% in 2018 and 9.4% in 2019. This is still, however, a low vacancy rate relative to other metropolitan areas. To put this into context we compare the vacancy rate against other regional and national competitor cities.

From the Census, we can get vacancy rate data at the Metropolitan Statistical Area (MSA) level. For consistency with other regions, we will use the Charlotte MSA vacancy rate when doing this analysis. This incorporates an additional four counties into the analysis and results in a slightly higher vacancy rate of 8.4%.

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Figure AI.18. Mecklenburg vs Suburban County Vacant Housing Units 2010-2019 60,000 50,000 40,000 30,000 20,000 10,000 0 2010

2011

2012

2013 Mecklenburg

2014

2015

2016

2017

2018

2019

Suburban

Source: CKCRE Tabulations of US Census Bureau American Community Survey 1 year estimates.

We compare the Charlotte MSA vacancy rate against nine regional and 12 national competitor MSAs. For the regional competitors, we selected the largest MSAs in North Carolina and South Carolina, as well as Atlanta, Georgia, and Richmond, Virginia. The national competitor MSAs were chosen either because they are similarly sized as Charlotte, routinely compete with Charlotte for economic development, or are geographically close. We also sought to select cities to provide some insight into all regions of the country.

Figure AI.19 shows the vacancy rate for the Charlotte MSA and the regional competitor MSAs. The Charlotte MSA has a lower vacancy rate than every competitor city, except for Raleigh. Figure AI.20 shows the vacancy rate for the Charlotte MSA and the national competitor MSAs, as well as the national average vacancy rate. The national average vacancy rate was 10.79%. Similar to Charlotte, many of the national competitor cities are in the 9-10% vacancy range. Most national competitor cities with lower vacancy rates than Charlotte experienced much larger house price growth rates over the last several years, as will be shown in Section IV of the report.

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Figure AI.19. Vacancy Rates in Regional Competitor MSAs 2019.

0.20 0.18 0.16 0.14 0.12 0.10 0.08 0.06 0.04 0.02 Asheville

Charleston

Charlotte

Columbia Greensboro

Raleigh

Richmond Spartanburg Wilmington

Source: CKCRE Tabulations of US Census Bureau American Community Survey 1 year estimates.

Figure AI.20. Vacancy Rates in National Competitor Cities 2019 18.00% 16.00% 14.00% 12.00% 10.00% 8.00% 6.00% 4.00% 2.00% US A

pa Ta m

An to ni o

Sa n

ra

m

en to

nd Sa c

Po rtl a

Na sh vi l le

ph is em M

po lis na

er

In di a

De nv

ti in na Ci nc

lo tte Ch ar

tin Au s

At la

nt a

0.00%

Source: CKCRE Tabulations of US Census Bureau American Community Survey 1 year estimates.

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E. Key Conclusions In Table AI.1, we combine previously reported statistics from the key measurements discussed above. As we noted in the first edition of this report, the Charlotte region’s population growth exceeded its housing unit growth for most of the 2010s. In economic terms, demand has outpaced supply and that has resulted in quickly increasing home prices and higher rents. Home prices and rents will not moderate until the region begins to produce new housing units in greater numbers than it has over the past eight years.

Table AI.1 Summary of Key Measures for the Charlotte Region (2010-2019) Data are from the American Community Survey.

Measure Total population Total housing units Vacant housing units Mean Household Income

2010 2,052, 515 863,1 67 93,17 9

2019 2,438, 105 995,6 79 90,53 6

Cha nge 385, 590 132, 512 2,643

$67,7 68

$92,3 76

24,6 08

% Change 18.79 % 15.35 % 36.31

2.84% %

Appendix II. Comparative Analysis We have focused on the housing dynamics within the Charlotte region, and have particularly focused on the various challenges the region faces. Without context, it can appear that the challenges and difficulties the region faces are unique or out of proportion to what other regions are facing.

In this section, we expand our analysis and compare the Charlotte region to other regional and national competitor cities. This analysis will necessarily rely upon broad-scale data, the vast majority of which coming from government sources such as the U.S. Census Bureau. For consistency across metropolitan areas, we use data reported at the Metropolitan Statistical Area

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(MSA) level. All data shown in this section comes from the American Community Survey oneyear estimates from 2005-2019 produced by the U.S. Census Bureau unless stated otherwise.

We will compare Charlotte’s performance in several key metrics with our competitor cities.

These metrics include: •

Population and population growth;

•

Land prices;

•

Median home prices;

•

Median multiple;

•

Median rents;

•

Median Price to Rent Ratio;

•

Cost-burden of housing;

Our analysis shows that although the Charlotte region faces challenges, the Charlotte region is doing fairly well overall when compared with many of our regional and national competitor cities. In particular, the region has relatively low-cost housing compared with many of our peer cities in both the region and nationally. The Charlotte housing market exhibits traits that are common among almost all cities that are rapidly growing.

For this analysis, we have selected eight regional competitor cities and 11 national competitor cities. The eight regional cities are Asheville, Greensboro, Raleigh, and Wilmington in North Carolina, Charleston, Columbia, and Spartanburg in South Carolina, and Richmond, Virginia.

Our goal is to examine each of the major economic centers in North Carolina and South Carolina.

We included Richmond because of its proximity and that it is closer in size to these regional cities as compared with the set of national competitors.

The national comparison set includes Atlanta, Austin, Cincinnati, Denver, Indianapolis, Memphis, Nashville, Portland, Sacramento, San Antonio, and Tampa. We choose the national competitor cities based on one of two criteria. First, we choose cities with populations similar to that of Charlotte, which led us to choose Austin, Cincinnati, Denver, Indianapolis, Portland, Sacramento, San Antonio, and Tampa. Second, we choose three cities, Atlanta, Nashville, and The State of Housing in Charlotte Report 2021

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Memphis, because of their proximity to Charlotte and because Charlotte frequently competes with them for economic development.

A. Population and Population Growth We begin by showing the population and population growth rate of each city in our regional and national comparison set in Figure AII.1 and Figure AII.2.

Figure AII.1 Regional Competitor MSAs Population 2019 and Population Growth from 2018 to 2019 3000000

4.00%

2500000 2000000

3.00%

2.63%

2.07%

1.84% 0.71%

0.67%

2.00% 1.05%1.00%

0.54%

0.00% -1.00%

-0.98%

1500000

-2.00% -3.00%

1000000

-4.00% -5.00%

500000 -6.30%

0 Asheville

Charleston

Charlotte

Columbia Greensboro

Raleigh

-6.00% -7.00%

Richmond Spartanburg Wilmington

Source: CKCRE tabulations of US Census Bureau ACS 1-year estimates.

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Figure AII.2 National Competitor MSAs Population 2019 and Population Growth from 2018 to 2019 7000000

3.00% 2.71% 2.63%

6000000

2.50% 2.00%

5000000 4000000

1.38%

1.14%

1.19%

1.37%

1.31%

3000000

0.57%

2000000

1.00%

0.79%

0.50%

0.09%

0.00%

-0.38%

1000000

1.66% 1.50%

-0.50%

pa Ta m

An to ni o

Sa n

en to ra

m

nd Sa c

Po rtl a

Na sh vi l le

ph is em M

po lis na

er

In di a

De nv

ti Ci nc

in na

lo tte Ch ar

Au s

At la

tin

-1.00% nt a

0

Source: CKCRE tabulations of US Census Bureau ACS 1-year estimates.

From Figure AII.1 we note that Charlotte MSA has the largest population and the fastest population growth in the region.16 Figure AII.2 shows that the Charlotte MSA has a population similar to the other comparison MSAs. The exception to this, of course, is the Atlanta MSA, which has more than twice the Charlotte MSA population. Atlanta is included because of its geographic proximity and because it is a frequent competitor for economic development. Figure AII.2 shows that the Charlotte MSA has one of the highest population growth rates among these national competitor cities, with only Austin growing at a faster rate.

B. Land Prices As was the case with our micro-analysis of the Charlotte housing markets, we start our comparative analyses across MSAs with land prices. We once again turn to Davis, Larson, Olinder, and Shui (2019) paper. As discussed in Section III, Davis, Larson, Olinder, and Shui

16

Note that the Raleigh MSA does not include Durham and Chapel Hill. Those cities are included in the Raleigh Combined Statistical Area (CSA) and brings the total population to a little over 2 million. We have elected to work with the MSA for consistency throughout the report.

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(2019) estimate the price of a hypothetical one acre of residential land in each county in the United States.

Figure AII.3 charts their estimate of the price of one acre of land in the central county for each MSA. We use the central county for each MSA because Davis, Larson, Olinder, and Shui (2019) do not report data at the MSA level, but only at the county level. As a result, we use the county-level results for the economic center of each MSA. For example, we use Mecklenburg County for Charlotte, Buncombe County for Asheville, and Wake County for Raleigh.

Figure AII.3 shows that, relative to regional competitors, Charlotte’s land prices are in the middle. They are significantly higher than Asheville, Columbia, and Greensboro, but are significantly lower than Charleston and Wilmington. Given the preponderance of coastal resort properties in those two cities, it is not surprising they have higher land values. What is somewhat surprising is that Raleigh has residential land values that are nearly $40,000 higher per acre than Charlotte. However, the gap between Raleigh and Charlotte has decreased a lot. The difference was $75,000 per acre in 2017.

This graph also shows that Charlotte’s land price growth rate over the period 2017-18 was robust at 13.5%, which is similar to the Greensboro land price growth rate, and significantly higher than those of Asheville, Charleston, Columbia, Raleigh, and Wilmington. It was only significantly lower than the rate in Spartanburg.

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Figure AII.5 Land Prices in Regional Competitor Cities 2018 and Growth Rate from 2017 to 2018 500000

20.00%

450000

18.08%

18.00%

400000

16.00%

350000

12.41%

300000 250000

9.57%

200000

14.00%

13.86%

13.55% 10.45%

12.00% 10.29%

8.80%

10.00% 9.05% 8.00%

150000

6.00%

100000

4.00%

50000

2.00%

0

0.00% Asheville

Charleston Charlotte

Columbia Greensboro

Raleigh

Richmond Spartanburg Wilmington

Source: CKCRE tabulations of US Census Bureau ACS 1-year estimates.

Figure AII.4 plots the land price in 2019 and land price growth estimates from 2018 to 2019 for each of the national competitor cities. These show dramatic differences in land prices across the country. In particular, Portland and Denver each has an estimated value for one acre of residential land over $1,000,000. While the land price in Charlotte is only higher than Cincinnati, Indianapolis, and Memphis, the land price growth rate in Charlotte is higher than most other national competitor cities, except for Indianapolis and Memphis.

Taken in this context, Charlotte has relatively low land prices compared with its national competitive cities. Charlotte has lower land prices than all but three of its national competitor cities (Cincinnati, Indianapolis, and Memphis). However, the land prices in Charlotte are increasing at a faster rate than most other national competitor cities. Should these trends continue, Charlotte’s house prices will also increase at a faster rate.

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Figure AII.4 Land Prices in National Competitor Cities: 2019 and Growth Rate from 2018 to 2019 1400000

20.00% 18.18%

1200000 13.55% 13.55%

1000000

18.00%

17.00%

16.00% 14.00%

13.13%

12.93%

10.44% 12.00%

800000

10.00%

7.93%

600000

3.88%

8.00%

400000

5.40%

5.41%

6.46%

6.00% 4.00%

200000

2.00%

pa Ta m

An to ni o

nd

Sa n

Po rtl a

Na sh vi l le

ph is em M

po lis na

er

In di a

De nv

ti in na Ci nc

lo tte Ch ar

m or e lti Ba

Au s

At la

tin

0.00% nt a

0

Source: CKCRE tabulations of US Census Bureau ACS 1-year estimates.

C. Median Home Prices Figure AII.5 presents the median home price for each of the regional competitor MSAs as well as the growth rate from 2018 to 2019. The Charlotte MSA median home price is lower than many of its regional competitors, except for Columbia, Greensboro, and Spartanburg. In terms of growth rates, the Charlotte MSA has a higher growth rate than most regional cities, except for Greensboro and Spartanburg. Note that, across all these regional cities, the house price growth rates have significantly increased during the last two years. For example, the median house price growth rate in Charlotte from 2016 to 2017 was only 4.78%.

Figure AII.6 presents the median home price for each of the national competitor MSAs. As was the case with land values, there are stark differences in the median home prices across these cities. Of the twelve 12 MSAs shown, the Charlotte MSA has the 5th lowest median home price.

Sacramento, Portland, and Denver each have median home prices almost double the median home price in the Charlotte MSA. However, Charlotte has one of the highest home price growth rates from 2018 to 2019.

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Figure AII.5. Charlotte MSA vs. Regional Competitor Cities Median Home Prices 2019 and Growth Rate from 2018 to 2019

300,000

9.00%

8.54%

8.00%

250,000

7.44%

7.30% 7.00%

6.73% 200,000

5.88%

6.00% 5.42%

5.06%

5.04%

150,000

5.00%

4.14%

4.00%

100,000

3.00% 2.00%

50,000

1.00%

0

0.00% Asheville

Charleston

Charlotte

Columbia Greensboro

Raleigh

Richmond Spartanburg Wilmington

Source: CKCRE tabulations of US Census Bureau ACS 1-year estimates.

Figure AII.6. Charlotte MSA vs. National Competitor Cities Median Home Prices 2019 and Growth Rate from 2018 to 2019 500,000

10.00%

450,000 400,000 300,000

8.57%

7.42%

7.26%

350,000

8.86%

8.63%

8.54%

6.81% 6.00% 5.00%

4.56%

200,000

8.00% 7.00%

6.73%

5.78%

250,000

9.00%

4.09%

4.00%

150,000

3.00%

2.79%

pa Ta m

An to ni o

h le ig

Sa n

Ra

nd Po rtl a

Na sh vi l le

ph is em M

na In di a

De nv

in na Ci nc

Ch ar

Au s

At la

po lis

0.00% er

0 ti

1.00% lo tte

50,000 tin

2.00%

nt a

100,000

Source: CKCRE tabulations of US Census Bureau ACS 1-year estimates.

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D. Median Multiple Analysis The median home price in each MSA only tells half the story. It gives us relative information on the price of a home, but it does not take into account that there can be very significant income differentials across cities too. Since our concern is around the relative cost of housing and the general economic competitiveness of the region, we next use a measure of housing affordability that incorporates both prices and incomes.

One of the most commonly used measures of housing affordability is the so-called median multiple. This multiple is defined to be the ratio of the median home price to the median income that is: 𝑀𝑒𝑑𝑖𝑎𝑛 𝑀𝑢𝑙𝑡𝑖𝑝𝑙𝑒 =

𝑀𝑒𝑑𝑖𝑎𝑛 𝐻𝑜𝑢𝑠𝑒 𝑃𝑟𝑖𝑐𝑒

𝑀𝑒𝑑𝑖𝑎𝑛 𝐼𝑛𝑐𝑜𝑚𝑒

Essentially this measures how many years’ income the median earner in a region would have to use to buy the median house. By convention a median multiple below three is considered “affordable”, a median multiple between three and four is considered “moderately unaffordable”, and a median multiple above four is considered “unaffordable.”

Figure AII.7 plots the median multiple for the regional competitor MSAs in 2019 and the growth rates of the multiple from 2018 to 2019. The Charlotte MSA is above the “moderately unaffordable” level of three in 2019. Among the nine cities in the chart, the Charlotte region is in the middle of the pack. So, while the median house price in the Charlotte MSA is high, the median income is relatively high too. Asheville and Wilmington have the highest median multiples, both of which are well into the “unaffordable” range. Of course, both of these cities are affluent retiree destination cities. Retirees, and especially affluent retirees, have accumulated wealth that they can put into buying a home, and are less reliant upon annual income to support their housing.

This may make the median multiple somewhat less meaningful in those two cities. Charleston, which also edges into the “unaffordable” range, may have a similar issue.

A concerning issue for Charlotte is, however, the median multiple is increasing very fast.

From 2018 to 2019, the median multiple increased by 3.253% from 3.41 to 3.52. The growth rate is the highest among all regional competitor cities. Should the trend continue, the Charlotte region will enter the “unaffordable” region fairly soon.

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Figure AII.7. Regional Competitor MSAs Median Multiples 2019 and Growth Rate from 2018 to 2019 5

4.00% 3.23%

4.5

3.12%

3.20%

4 1.80%

3.5

2.60%

3.00% 2.00%

2.02%

3

1.00% 0.07%

2.5

0.00%

2 1.5

-1.00%

1 0.5

-1.21%

-2.00%

-2.21%

0

-3.00% Asheville

Charleston

Charlotte

Columbia

Greensboro

Raleigh

Richmond Spartanburg Wilmington

Source: CKCRE tabulations of US Census Bureau ACS 1-year estimates.

Figure AII.8 shows the median multiple for the national competitor MSAs. Compared with other national competitor cities, Charlotte appears to be in the middle regarding the median multiple. Denver, Portland, and Sacramento have the highest level of the median multiple, that is, the highest level of “unaffordability.” The median multiple in Charlotte is also lower than Austin, Nashville, and Tampa, but slightly higher than Atlanta, Memphis, and San Antonio.

Except for Denver and Memphis, all other MSAs have seen an increase in the median multiple from 2018 to 2019. However, Charlotte is among the cities that has the highest levels of growth of the median multiple.

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Figure AII.8 National Competitor MSAs Median Multiples in 2019 and Growth Rate from 2018 to 2019 6

8.00%

7.39%

5

6.00%

4

3.88%

3

3.23%

4.00%

3.94% 2.62%

1.41%

1.39%

0.61%

2

1.80%

2.66% 2.00% 0.00%

-0.42% -1.81%

1

-2.00%

pa Ta m

An to ni o

Sa n

en to ra

m

nd Sa c

Po rtl a

Na sh vi l le

ph is em M

po lis na

er

In di a

De nv

ti in na Ci nc

lo tte Ch ar

Au s

At la

tin

-4.00% nt a

0

Source: CKCRE tabulations of US Census Bureau ACS 1-year estimates.

E. Median Rents The comparisons above all focus on the owner-occupied markets. The rental markets are a major component of the housing market in every city and are another useful metric for comparing housing markets across regions. Figure AII.9 plots the median rent for each of the regional competitive MSAs in 2019 as well as the growth rate in rents from 2018 to 2019. Note that the Census Bureau tabulates these median rents for all renters, so this includes households renting single-family homes and duplexes as well as those renting apartments.

The Charlotte MSA has the fifth-highest median of the regional competitors. Only Charleston, Raleigh, Richmond, and Wilmington were higher. However, the growth rate of median rent in Charlotte from 2018-19 is modest.

Figure AII.10 plots the median rents relative to the national competitor cities. In contrast to the regional markets, at the national level, there are multiple competitor cities, specifically Austin, Denver, Portland, and Sacramento, where the median monthly rent exceeds $1,000. Indeed, the Denver median monthly rent is over $1,200. This is nearly 50% higher than the median rent in

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the Charlotte MSA. Further, the growth rate in median rents in these high-rent MSAs tends to be high: Denver, Austin, and Portland each have annual growth rates above 6%.

On the national stage, the Charlotte MSA median rent is moderate. Of the 12 MSAs compared, the Charlotte MSA has the fifth-lowest median rent. Its median rental growth rate of 1.70% also puts it at the lower end of the distribution.

Figure AII.9 Regional Competitor MSAs Median Rents 2019 and Growth Rate from 2018 to 2019 1400 1200

12.00% 10.42%

10.00% 8.00%

7.13%

1000

6.00%

800 600

3.31%

2.49%

1.70%

4.00% 2.44%2.00%

-0.09%

400

-1.14%

200

0.00% -2.00%

-3.77%

-4.00%

0

-6.00% Asheville

Charleston

Charlotte

Columbia Greensboro

Raleigh

Richmond Spartanburg Wilmington

Source: CKCRE tabulations of US Census Bureau ACS 1-year estimates.

F. Price to Rent Ratio A common metric used to measure the relative value of buying versus renting a home is the Price to Rent Ratio, which is the ratio of the median home price in a region to the median annual rent in the region. That is:

𝑃𝑟𝑖𝑐𝑒 𝑡𝑜 𝑅𝑒𝑛𝑡 𝑅𝑎𝑡𝑖𝑜 =

𝑀𝑒𝑑𝑖𝑎𝑛 𝐻𝑜𝑚𝑒 𝑃𝑟𝑖𝑐𝑒

𝑀𝑒𝑑𝑖𝑎𝑛 𝐴𝑛𝑛𝑢𝑎𝑙 𝑅𝑒𝑛𝑡

A higher ratio means that home prices are high relative to rents, and a low ratio means home prices are inexpensive relative to rents. When the ratio is high it means that a region is more

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favorable for renting, and when it is low the region is more favorable to home buying. When used to compare across regions it gives a relative sense of whether renting or buying is more favorable.

Figure AII.10 National Competitor MSAs Median Rents 2019 and Growth Rate from 2018 to 2019 1600

6.00%

1400

5.00%

4.86%

1200

4.09%

4.04%

1000

4.00% 3.42%

3.12%

2.81%

800

2.00% 1.58% 1.00%

1.70% 1.19%

1.08%

400

0.00%

-0.38%

pa Ta m

An to ni o

ra

m

en to

nd Sa c

Po rtl a

Na sh vi l le

ph is em M

na

po lis

-1.00%

In di a

ti in na Ci nc

lo tte Ch ar

tin Au s

nt a At la

er

-0.70%

0

De nv

200

Sa n

600

3.00%

Source: CKCRE tabulations of US Census Bureau ACS 1-year estimates.

Figure AII.11 shows the Price to Rent Ratio for each of the regional competitor cities in 2019.

The ratio for Charlotte is lower than that for Asheville, Charleston, Raleigh, Richmond, and Wilmington are each a little higher than in Charlotte, indicating that renting is more favorable in those MSAs than in Charlotte. In contrast, Columbia, Greensboro, and Spartanburg all have lower Price to Rent Ratios than the Charlotte MSA, indicating that in those MSAs buying is more favorable compared to Charlotte. However, Charlotte has a third-highest growth rate of the Price to Rent ratio of 6.72%.

At the national level, the results are more dramatic. Figure AII.12 plots these ratios for each of the national competitor MSAs. The Denver, Portland, and Sacramento MSAs each have ratios that are 25% or more higher than Charlotte’s indicating that renting is significantly more favorable relative to purchasing a home than it is in Charlotte. In contrast, San Antonio and Tampa both have ratios a good bit lower than Charlotte’s, indicating a more favorable environment for home buying there than in Charlotte. Again, keep in mind that these are just The State of Housing in Charlotte Report 2021

86

relative measures. As shown in Figures IV.6 and IV.10, both median home prices and median rents are more expensive in Denver, for example, than in Charlotte. The price to rent ratio is telling us that given rents and prices in both Charlotte and Denver, the environment for renting in Denver is more favorable than in Charlotte.

G. Cost-Burdens One of the challenges facing any region is the degree to which its residents are cost-burdened when obtaining housing. As we discussed in Section III, the normal definition of being costburdened is spending more than 30% of one’s gross income on housing costs including utilities.

Figure AII.11. Regional Competitor MSAs Median Home Price to Median Rent Ratio 2019 and Growth Rate from 2018 to 2019

25

10.00%

9.15% 6.63%

20

8.00% 6.82%

6.72%

6.00% 4.75% 4.00%

15

2.00%

1.60% 10

0.29% -1.20%

5

0.00% -2.00% -4.00%

-4.11% 0

-6.00% Asheville

Charleston

Charlotte

Columbia

The State of Housing in Charlotte Report 2021

Greensboro

Raleigh

Richmond

Spartanburg Wilmington

87

Figure AII.12. National Competitor MSAs Median Home Price to Median Rent Ratio 2019 and Growth Rate from 2018 to 2019 30

9.00% 8.02%

25

8.00%

7.46%

7.00%

6.72% 20

6.00% 4.96%

15 10

4.98%

4.58%

2.58%

4.00% 3.00%

2.44%

2.00%

1.64%

1.58%

5

1.00% pa Ta m

An to ni o

Sa n

en to ra

m

nd Sa c

Po rtl a

Na sh vi l le

em

ph is

0.00% M

po lis na

er

In di a

De nv

ti in na Ci nc

lo tte Ch ar

Au s

At la

tin

0.05% nt a

0

5.15% 5.00%

Source: CKCRE tabulations of US Census Bureau ACS 1-year estimates.

We begin by considering the proportion of renters in each region that are cost-burdened. Figure AII.13 shows the percentage for each of the regional competitor MSAs of cost-burdened renters.

For the Charlotte MSA, 45% of all renters meet the definition of being cost-burdened. What is perhaps more surprising is that this is the second-best ratio in the data set. In every MSA in the region, at least 40% of the renters meet the definition of being cost-burdened.

Figure AII.14 shows the same measure for the national MSA competitor set. Again, relative to most of its peer cities, the Charlotte MSA has one of the lower cost-burdened rates, although it is still high in absolute terms. Memphis, Portland, Sacrament, and Tampa each have more than 50% of their renters meeting the definition of being cost-burdened. Only Cincinnati has a lower percentage of cost-burdened renters than the Charlotte MSA. It is also possible to construct a similar measure for homeowners, that is, to determine which percentage of homeowners pay more than 30% of the gross income toward housing expenses.

One would expect this number to be relatively low because most lenders will not originate mortgages where the monthly payment is more than 28% of the borrower’s income. Still, conditions can change over time, especially with respect to income. Additionally, a household could take on a second mortgage after the origination of the first loan, which could increase their total housing expense above the 30% cost-burdened level.

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Figure AII.13 Regional Competitor MSAs Percentage of Cost-Burdened Renters 2018 and 2019 60% 50% 40% 30% 20% 10% 0% Asheville

Charleston

Charlotte

Columbia Greensboro 2018

Raleigh

Richmond Spartanburg Wilmington

2019

Source: CKCRE tabulations of US Census Bureau ACS 1-year estimates.

Figure AII.14 National Competitor MSAs Percentage of Cost Burdened Renters 2018 and 2019 60.00% 50.00% 40.00% 30.00% 20.00% 10.00%

2018

The State of Housing in Charlotte Report 2021

pa Ta m

An to ni o Sa n

Sa c

ra

m

en to

nd Po rtl a

Na sh vi l le

ph is em M

po lis na

er

In di a

De nv

ti in na Ci nc

lo tte Ch ar

tin Au s

At la

nt a

0.00%

2019

Source: CKCRE tabulations of US Census Bureau ACS 1-year estimates.

89

Figure AII.15 presents the percentage of cost-burdened homeowner households for the regional competitor MSAs. The percentages are, in fact, much lower than was the case with the renter population. The Charlotte MSA has the third-lowest percentage of cost-burdened homeowners, with only Raleigh and Spartanburg having a lower rate.

Figure AII.16 presents the same metric for national competitor cities. As was the case with the regional competitor MSAs, the percentage of cost-burdened homeowners is lower in every MSA. Charlotte has one of the lowest percentages, with only Cincinnati and Indianapolis having lower percentages. Perhaps not surprisingly, Denver, Portland, Sacramento, and San Antonio have the highest percentages. Sacramento has the highest rate, with nearly one-third of all homeowners meeting the criteria for being cost-burdened.

Figure AII.15 Regional Competitor MSAs Percentage of Cost Burdened Homeowners 2018 and 2019 35.00% 30.00% 25.00% 20.00% 15.00% 10.00% 5.00% 0.00% Asheville

Charleston

Charlotte

Columbia

Greensboro

2018

The State of Housing in Charlotte Report 2021

Raleigh

Richmond

Spartanburg Wilmington

2019

Source: CKCRE tabulations of US Census Bureau ACS 1-year estimates.

90

Figure AII.16 National Competitor MSAs Percentage of Cost Burdened Homeowners 2018 and 2019 35.00% 30.00% 25.00% 20.00% 15.00% 10.00% 5.00%

2018

pa Ta m

An to ni o Sa n

ra

m

en to

nd Sa c

Po rtl a

Na sh vi l le

ph is em M

po lis na

er

In di a

De nv

ti in na Ci nc

lo tte Ch ar

tin Au s

At la

nt a

0.00%

2019

Source: CKCRE tabulations of US Census Bureau ACS 1-year estimates.

H. Summary Comparing the performance of the Charlotte MSA against a series of competitive regional and national MSAs helps put in perspective many of the challenges that Charlotte faces. It is clear, for example, that land prices, housing prices, and rents are growing everywhere. There are simply national trends toward higher land and housing prices, and those trends will manifest in Charlotte.

The Charlotte MSA is a growing region, with one of the highest growth rates of the national and regional comparison sets. While the levels of the prices are still modest in Charlotte, they are growing at high rates – higher than most regional and national competitor cities. Should the trend continue, the Charlotte region may soon face significant challenges regarding housing affordability.

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