Mortgage Foreclosure Analysis Cincinnati

Analysis of Spatial Patterns of Mortgage Foreclosures in Cincinnati Identifying contributing causes to areas of high foreclosure rates

By: Serena Barker February 2011

Data Sources: CAGIS, Cincinnati Court Index, Hamilton County Auditor, Ohio Department of Development, US Census Bureau, Working in Neighborhoods

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Project Objective: The purpose of this project is to identify hot spots of high foreclosure rates and connect these areas systematically to identify common characteristics. This tool will help implement strategies to reduce foreclosure rates by identifying areas where Transit-Oriented-Development or other development tools could be utilized.

Research Background: Economists have identified the subprime mortgage crisis as the financial factor leading to high mortgage default and foreclosure rates; however, few have identified spatial characteristics contributing to the crisis. The 2007–2010 financial crises identified one of the first instigators of the subprime mortgage crisis, characterized by a rise in subprime mortgage delinquencies and foreclosures. But it does not describe the decline of suburban cul-de-sac development over recent years. It appears there have been structural changes in the housing market, shifting the way people chose to work and live. In most metropolitan areas, only 5-10 percent of the housing stock is located in walkable urban areas. Yet recent consumer research by Jonathan Levine of the University of Michigan and Lawrence Frank of the University of British Columbia suggests roughly one in three homeowners would prefer to live in these types of places. Using spatial analyst we can try to identify and connect this theory with current foreclosure hot spots in Cincinnati.

A shift in recent years which reflect changes in foreclosure trends can help identify changes in living patterns. In the past, high rates of foreclosures occurred primarily in low- to moderate-income inner ring communities. In 2009, foreclosure trends began to shift towards higher end homes as sheriff sales began to spread into higher income suburbs in the outer rings of Cincinnati, beginning in 2008 and continuing into 2010. As you can see in Table 1, communities such as Sharonville and Reading which once were very low in the number of foreclosures have been experiencing an exponential growth in foreclosure rates in recent years. These communities typically contained higher income housing are now too being affected by the mortgage sweep. This table reflects the changes in the Hamilton County housing market in recent years however, in this study I will only be analyzing the city of Cincinnati.

Table 1: Top 15 Hamilton County Municipalities by Number of Foreclosures, 2007-2009 2009 Ranking City/Township Completed Completed Foreclosures, 2009 Foreclosures, 2008 (Ranking) 1 Cincinnati 1,151 1,355 (1) 2 Colerain Township 229 315 (2) 3 Cheviot 132 43 (10) 4 Springfield 124 217 (3) Township 5 Green Township 121 147 (4) 6 North College Hill 89 67 (8) 7 Forest Park 79 137 (5) 2

Completed Foreclosures, 2007 (Ranking) 1,514 (1) 267 (2) 56 (9) 197 (3) 114 (5) 78 (7) 143 (4)

8 Norwood 64 94 (6) Table 1: Top 15 Hamilton County Municipalities by Number of Foreclosures, 2007-2009 2009 Ranking City/Township Completed Completed Foreclosures, 2009 Foreclosures, 2008 (Ranking) 9 Delhi Township 58 81 (7) 10 Anderson Township 56 47 (9) 11 Sycamore Township 46 32 (15) 12 Springdale 36 33 (14) 13 Harrison Township* 31 42 (11) 14 Reading 30 20 (21) 15 Sharonville 25 15 (31) Total 2,271 2,645

91 (6) Completed Foreclosures, 2007 (Ranking) 72 (8) 49 (10) 48 (11) 25 (18) 43 (12) 30 (16) 12 (32) 2,739

Data Sources: CAGIS 2009, Cincinnati Court Index, Working in Neighborhoods

Study Area: This study is focused on finding specific geographic indicators in areas containing high rates of foreclosure in Cincinnati. The study hypothesizes that housing stock located away from key commercial districts tends to be hot spots for rising foreclosure rates. Multiple data sets will be used to identify the commercial districts including areas within close proximity to high concentration of jobs and/or access to public transit such as bus routes.

Hypothesis: Hypothesis: To test the hypothesis, the research will look into connections between regions of high foreclosure rates in housing development to access to major employment. The hypothesis null states that there are no correlations between foreclosure and employment access.

The study will focus on two main characteristics to define the hypothesis; public service availability and ease of access to employment. Concentration areas will be defined by the levels of which regions are disconnected to major employers and the maximum travel distance willing and/or able to access these regions. Regions inside of these districts will be compared and contrasted to high concentration of foreclosures to validate the hypothesis.

Based on the hypothesis, housing stock located outside buffer regions containing major employers and mass transportation contain higher rates of foreclosures due to accessibility issues.

Connections between hot spots and these regions will be tested for validation or nullified. First major employers will be defined and identified on the map and a buffer surrounding that region will be defined. Breaking point analysis will establish the distance between the major employer buffer regions and the surrounding communities and will define the maximum commuting distances between each market area. Public services will be overlaid with that buffer region to test if other factors affect these hot spots such as proximity to mass transit.

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Hypothesis (Ho): High rates of foreclosures occur in housing markets outside of buffer regions containing mass transit and/or within close proximity to major employers are less than or equal to regions within these buffer regions. Transit oriented development is more ideal for this region.

Hypothesis Null (Ha): High rates of foreclosures occur more than or equal to regions containing mass transit or within a buffer region within close proximity to major employers. Transit oriented development is not a deciding factor for consumers and therefore would not benefit.

To identify the maximum distance a population is willing to travel for employment and other commuting trends, current commuting trends were reviewed. The table below identifies that 90.3% of employees over the age of 16 commute to work by car in Cincinnati. However for the top employers in Cincinnati including: manufacturing of which 7.4% of employees use public transportation to get to and from work, retail trade- 8.5%; finance -11.2%, and in restaurant services- 18% use public transportation. Therefore despite the fact that the majority of employees in Cincinnati are commuting to work by car, there appears to be a significant proportion who are traveling by public transport and therefore are in dire need of access to these services.

Table 2: Means of Transportation to Work -American Community Survey, 2005-2009 Car, truck, or Car, truck, or van -- drove van -Subject Total alone carpooled Industry Agriculture, forestry, fishing and hunting, 0.2% 0.2% 0.2% and mining Construction 5.4% 5.1% 9.7% Manufacturing 13.9% 14.8% 12.0% Wholesale trade 3.8% 3.8% 3.2% Retail trade 11.6% 11.7% 11.8% Transportation and warehousing, &utilities 5.3% 5.6% 3.4% Information and finance and insurance, and 10.7% 10.9% 9.7% real estate and rental and leasing Professional, scientific, management, 11.0% 10.2% 11.3% administrative & waste management Educational services, health care & social 21.1% 21.5% 18.9% assistance Arts, entertainment, recreation, &accommodation and food services Other services (except public administration) Public administration Armed forces

Public transportation (excluding taxicab) 0.1% 1.9% 7.4% 2.1% 8.5% 5.3% 11.2% 14.2% 22.6%

9.3%

8.4%

13.0%

18.0%

4.2% 3.4% 0.1%

4.2% 3.5% 0.1%

3.5% 3.1% 0.1%

2.7% 5.8% 0.1%

Data Sources: US Census Bureau: American Community Survey

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Subject Means of Transportation to Work Car, truck, or van Drove alone Carpooled In 2-person carpool In 3-person carpool In 4-or-more person carpool Workers per car, truck, or van Public transportation (excluding taxicab) Walked Bicycle Taxicab, motorcycle, or other means Worked at home

Total 90.3% 81.5% 8.8% 7.2% 0.9% 0.6% 1.06 3.2% 2.2% 0.1% 0.7% 3.4%

Data Sources: US Census Bureau: American Community Survey

Data: Data: Foreclosure: Foreclosure data was collected from The Cincinnati Court Index and entered into an Excel spreadsheet. The data extracted were specifically Sheriff Sale Listings which are the finalized sale on a defaulted mortgage loan. Sheriff Sale data was only collected for the prior three-month timeframe frame from October to December due to the time constraints of the project and the time it took to manually enter each individual foreclosure into the database.

Major Employers: The top ten employers within Cincinnati were identified by a report generated by the Ohio Department of Development in April 2009. By identifying the top employers I was able to locate the addresses for each major employer and geo-locate them on the map. The top ten employers are identified below: Table 3: Top 10 Employers in Cincinnati, 2009 Rank Company 1 Kroger Co 2 Catholic Healthcare Partners 3 Procter & Gamble Company 4 Cincinnati Children's Hospital Medical Center 5 TriHealth 6 Fifth Third Bancorp 7 Health Alliance 8 University of Cincinnati 9 Frisch's Restaurants 10 Cintas Corp Data Sources: Ohio Department of Development

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Headcount Sector 38000 Retail Food Stores 28200 Health 14000 Manufacture 10350 Health 9850 Health 9500 Finance Bank 9500 Health 9500 Education 4750 Restaurants 3800 Manufacture

The data was then organized into two categories: one for the professional sector including the top Health, Manufacturing, and Finance corporations and one for the retail and restaurant sector which research identified The Krogers Company and Frisch’s Restaurants Corporation as the two main employers in this sector. The retail and restaurant sector was not as centralized as the professional so therefore they had to be separated into to catagories.

Bus Stops: Bus route information was provided by CAGIS which was also used to find neighborhood boundaries and was used to establish a georeference for the raw data in this analysis.

Time Period: This report is based on Cincinnati properties sold at Sheriff Sales between October 1, 2010 and December 31, 2010.

StatisticalMethods: Methods: Statistical Straight Line Regression Model: Yi= + i i + i, i=1,……,n Identifies validity to the in independent variables tested the analysis. Where the dependent variable is the foreclosures and the independent variables are the major employers, Frisch’s Restaurants, Krogers Stores, and bus stops. This model is a randomized experiment testing the correlation between the dependent and independent variables. The model will produce R2 values which will show the degree two variables are correlated. The larger the R2 value the more correlated the data is.

Bivariate Correlation Scatter Plot: A scatter plot shows the relationship between two variables by showing direction and strength of a linear relationship. A bivariate plot will show a strong or weak positive, negative, or no relationship correlation between two variables. Using this method will help identify the key variables to analyze when utilizing the straight line regression model. A strong positive relationship between foreclosures and any one of the independent variables will identify the data that needs to be further analyzed in the research.

Breaking Point: Identifies a buffer region surrounding major employers or identifies the boundary distance. The breaking point finds the Dij or the distance between two nodal regions divided by the square root of the population of nodal region j over i. Nodal regions are identified as concentrated major business regions specifically located in the downtown/Riverfront and Avondale neighborhoods.

To find the most accurate breaking point between the two employment buffers I found the average distance between all the employers (Dij) and average population of neighborhoods employers are located in and divided each individual population by the average (Pj/Pi). Then I 6

found the breaking point for each employer and averaged the total breaking point amount and found the breaking point between the major employers to be a 2.09 Mile buffer region.

GIS Models: Testing Data Straight line regression analysis was implemented through GIS to identify spatial relationships between dependent variables. Two regression models were used in the analysis, Ordinary Least Squares regression (OLS) and Geographically Weighted Regression (GWR).

Ordinary Least Squares Regression: OLS is a model which tests regression by identifying a correlation between key dependent variables, however it best identifies relationships on dependent variables that are spatially centralized.

Geographically Weighted Regression: GWR is spatial regression method which allows the model to determine relationships in key dependent variables across a study area.

Scatter Plot Matrix: Scatter plots identifies key dependent variables in the analysis by examining linear relationship between two variables.

Spatial Autocorrelation: Spatial Autocorrelation tool test whether or not the regression residuals (or degree of error for each point) exhibit a random spatial pattern.

Testing the Data: Scatter Plot Matrix

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In testing the correlation between dependent variables, the data only identified two of the four dependent variables to have any correlation. The scatter plot shows that both foreclosure to bus routes and foreclosures to major employers have a positive relationship. However, the scatter plot containing foreclosure to bus stop shows the strongest relationship indicating it is the most important variable in the analysis.

Data Results: Least Ordinary Square (LOS)

The above map graphically represents the results from LOS module which shows the standard deviation of a three variable (1 independent, 2 dependent) model based on neighborhoods within Cincinnati. The two dependent variables are based on the count of bus stops and major employers within a neighborhood. The red neighborhoods are under predictions (where the actual number of bus stops and major employers is higher than the model predicted); the blue neighborhoods are over predictions (actual bus/major employer density is lower than predicted).

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Testing the Data: Least Ordinary Square (LOS) To test whether or not the residuals (degree of error) exhibits a random spatial pattern I used the Spatial Autocorrelation tool. The Spatial Autocorrelation tool shows the z-score results in a bell curve to test if the data has complete spatial randomness. If there were clustering or over dispersion of the under/over predictions, it means the data was missing key explanatory variables.

However, results reflected the data exhibited fairly randomly dispersion, meaning the information accepted the hypothesis that bus routes and major employers affected where foreclosures were located.

The results of LOS show multiple statistical indicators reflecting the accuracy of the data including standard error, probability, T-test, and variance inflation factor (VIF). The standard error for the bus variable is very small, validating the scatter plot analysis which showed a strong relationship between the dependent and independent variable. Both bus and major employer variables have virtually no probability indicating it is completely random. The T-test on the bus variable indicates wider differences between the means, indicating less error in the data. Also the VIF tests redundancy among your explanatory variables, both variables contain smaller VIF (less than 7.5) indicating less biasness or over-count of the variables.

The Jarque-Bera, Koenker, and Adjusted R2 are diagnostic tools included in the LOS model which also indicated the accuracy of the data. The adjusted R2 value ranges from 0 to 1.0 and tells how much of the variation the dependent variable has been explained by the model. The results from the LOS map was 0.68 (higher than 0.5), indicating a fairly decent correlation between bus stop/major employers to foreclosures. The Jarque-Bera test measures whether or not the residuals 9

(degree of error) from a regression model are normally distributed. The Koenker test indicates spatial relationships between the two variables and the foreclosure data. The test result produced a fairly high degree of freedom (0.46) indicating that bus stops and major employers are not centrally located. Using the GWR model, test results will improve.

All tests ran by the LOS model which contained an asterisk next to the results indicated statistical significance. The only test which did not contain the asterisk was the Jarque-Bera test.

Data Results: Geographically Weighted Regression (GWS) The GWR Tool was used to better represent the spatial relationships between bus stops/major employers to foreclosures. The maps produced are based on a two variable (bus stops, major employers) model which was represented separately in the symbology to identify each indicator individually. Orange areas reflect areas containing less standard deviation indicating a stronger relationship between the number of bus stops/major employers to foreclosures. Blue areas have larger standard deviations indicating the information is less trustworthy. Also, a comparison of the adjusted R2 value in the GWS to the LOS reflects an increase to 0.711 which means it is a more accurate relationship between the variables.

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Final Conclusion: Weighted Sum The statistical analysis was used to graphically test the validity of the hypothesis of this study by visually comparing foreclosure hot spots to nodal regions containing large densities of bus routes and major employers. Utilizing the breaking point equation to calculate the buffer regions for the major employer nodes and creating a kernelled raster of the bus stops based on a 0.5 mile radius (maximum distance willing to walk to a bus stop) visually reflects the comparison. A weighted sum of bus routes to major employer nodes was established based on the R2 values from the GWR report .

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The results of this analysis validated the hypothesis that high rates of foreclosures do in fact occur in housing markets outside of buffer regions containing mass transit and/or within close proximity to major employers. However, the 0.71 Adjusted R Squared results indicated there were other key variables which were not evaluated in the research. Had the R2 results indicated 1.0, then we could have concluded that we have found the indefinite explanation for the cause of foreclosures within Cincinnati, OH. Although previously mentioned, there are many other financial variables which also explain the causes of foreclosures and other unexplored spatial explanations such as gentrification.

However for the needs of this study, the overall conclusions support the hypothesis.

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