Historic Home Rehabilitation in Beall's Hill

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Historic Home Rehabilitation in Bealls Hill Impact Assessment of Residential Rehabilitation Investments by the Historic Macon Foundation

Report By LandLink Analytic Services Author: Aaron Scherf May, 2017

LandLink Holdings, LLC https://landlink.systems landlinksystems@gmail.com 478.227.0670

Historic Macon Foundation http://www.historicmacon.org/ info@historicmacon.org 478.742.5084

Contents Introduction ................................................................................................................................................... 2 Context of Research and Motivation for Study ............................................................................................ 2 Historic Macon Foundation ...................................................................................................................... 2 Development in Beall’s Hill ...................................................................................................................... 4 Demography of Beall’s Hill ...................................................................................................................... 5 Review of Relevant Literature ...................................................................................................................... 7 Need for Economic Impact Studies in Historic Preservation ................................................................... 7 Property Value Assessment Methodology ................................................................................................. 8 Difference-in-Differences Regression Methodology ................................................................................. 9 Spatial Dependence, Neighborhood Effects, and Time-Series Effects .................................................... 10 Best Practices in Community Revitalization ........................................................................................... 12 Research Hypothesis and Framework ......................................................................................................... 13 Data Sources and Methodology of Analysis ............................................................................................... 13 Organization of Data for Analysis .......................................................................................................... 14 Spatial Dependency Calculations using GIS .......................................................................................... 14 Regression Methodology Overview ........................................................................................................ 15 Explanation of Variables ........................................................................................................................ 16 Difference-in-Difference Regression Formulas ...................................................................................... 17 Research Results and Interpretation ............................................................................................................ 18 Results of Regression 1 – Citywide Residential Property Values ........................................................... 19 Results of Regression 2 – Surrounding Neighborhoods Property Values .............................................. 21 Regression 3 – Beall’s Hill Property Values .......................................................................................... 22 Mapping Results through GIS ................................................................................................................. 24 Conclusions for Stakeholders and Preservation Economics Field .............................................................. 26 Bibliography ............................................................................................................................................... 29

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Introduction The purpose of this research is to analyze the investments made by the Historic Macon Foundation (HMF) in rehabilitating properties in the neighborhood of Beallâ€&#x;s Hill in Macon, Georgia. The timeline considered spans from 2008 to 2016, due to both data constraints in the public record and to coincide with the period in which Historic Macon actively acquired properties in the area. The methodology of this study compares residential parcels which have received investment from HMF with the remainder of the neighborhood, using the assessed property values for the entire city of Macon as a control for trends in the surrounding housing market. A difference-in-differences test is applied to determine whether HMF had a significant effect on the value of those properties receiving investment, while a spatial autocorrelation variable is utilized to both control for and measure spillover effects. The objective of this study is twofold: to evaluate Historic Maconâ€&#x;s revitalization efforts to inform their future property investments, and to contribute a replicable assessment methodology for similar historic preservation and neighborhood development organizations. Conclusions for both Historic Macon in particular and the field of community revitalization as a whole are considered, along with recommendations for improvements in future studies.

Context of Research and Motivation for Study Historic Macon Foundation The Historic Macon Foundation is a non-profit preservation and revitalization organization. Their focus ranges from traditional preservation strategies such as the management of national historic registry districts and local landmarks to innovative property rehabilitation and neighborhood heritage documentation. HMF was created from the merger of two groups in July of 2003: the Middle Georgia Historical Society, an academically oriented group focused on the narrative of central Georgia, and the Macon Heritage Foundation, a nonprofit focused on preserving the historic architecture of the city. The Historic Macon Foundation continues their work of preserving the physical character, cultural sites, and stories of Macon, Georgia. From this rich tradition, HMF has expanded its work to include neighborhood revitalization and community development through concentrated residential property investments in the areas surrounding Tattnall Square Park, a central anchor for the local community. These include: Huguenin Heights, where the Macon Heritage Foundation restored 16 single family homes in 1994; Tattnall Square Heights, where HMF has developed and sold 17 houses since 2000; and LandLink Analytic Services


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Beall‟s Hill, a much larger residential district in which HMF has made a concentrated commitment, developing 38 properties since 2008. Historic Macon‟s work in Beall‟s Hill operates through a partnership involving the city government, Land Bank Authority, and Mercer University. This coalition of stakeholders, united under HMF‟s mission of preserving the neighborhood and its historical significance to the city, has made significant improvements to the housing stock and appearance of the neighborhood over the past eight years. The current objective of the foundation is to expand their revitalization work, promote the story of Beall‟s Hill‟s recovery, and demonstrate that preservation is not just important for the culture and aesthetic of a city but also an efficient means of improving the economy through stimulation of underappreciated residential districts. As such, the motivation of this study is to evaluate the impact of Historic Macon‟s investments on the change in local property values. This assessment aims to serve as both a tool to guide HMF‟s future property rehabilitation projects and similar historic preservation initiatives in peripheral urban neighborhoods. The objective of contributing a guideline for assessment to the field of preservation economics fits with the mission of Historic Macon, which aims to be the premier historic preservation organization in the nation.

Figure 1: Outlines of Neighborhoods with Historic Macon Foundation Investment, with Relative Property Values for 2016 Expressed through Graduated Blue Shading and HMF Properties in Beall‟s Hill Overlaid with Green LandLink Analytic Services


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Development in Beall’s Hill The neighborhood of Beall‟s Hill has a long history of bringing people together. One of the first areas in the city to integrate, it remains a vibrant mixed income district. Young families new to the city live next door to couples who have owned their home for fifty years. This diversity has created many competing visions for the neighborhood‟s development over the years, with a mix of actors driving changes in both residential and public space. Large scale revitalization work began in earnest in 1998, when the city partnered with Mercer University and the Macon Housing Authority to rebuild the Oglethorpe Homes public housing development through a HOPE VI grant, awarded in 2001. The Beall‟s Hill Redevelopment Corporation directed restoration efforts until 2007, when the organization came under investigation for its obfuscated financial management practices. While no misconduct was uncovered, the investigation motivated Mercer University and the city government to redefine the nature of their partnership under a new coalition called Historic Hills and Heights. Keenly aware of the potential pitfalls of urban development, the new group decided that preserving the cultural character of the area should be its first priority. As such, the coalition invited Historic Macon to extend their residential revitalization work to the neighborhood. The shift launched a new phase in Beall‟s Hill‟s development. Mercer University expanded its down payment assistance program for faculty to purchase homes in the area while simultaneously forming the College Hill Corridor Commission, a grassroots neighborhood revitalization effort which would go on to make significant investments in Beall‟s Hill and the surrounding area. After receiving grant funding from the Knight Foundation, the professionally staffed College Hill Alliance took over the placemaking and strategic vision of Beall‟s Hill in 2010, leaving Historic Macon to focus on property developments. To date, HMF has acquired and invested in 38 properties in the neighborhood, for a total of $5,906,307 in residential improvements. Financing for these projects has come primarily from the organization‟s revolving loan fund, which was supplemented in 2014 with a $3 million grant from the Knight Foundation. Historic Macon‟s continued investments in the housing stock of the neighborhood, combined with a $2 million infrastructure improvements plan recently announced by the city government, makes the economic outlook of Beall‟s Hill highly promising. The most critical component of the neighborhood‟s future, however, is the preservation of long-term residents and the social capital they invest in the area. Fortunately, the unique nature of the public-civic partnership directing Beall‟s Hill‟s LandLink Analytic Services


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development makes all property acquisitions and investments accountable to the existing neighbors, preventing the gentrifying effects of unrestricted private sector development witnessed in other historic urban districts around the country. The success of HMF‟s rehabilitations has made the property market in Beall‟s Hill more and more attractive to speculative investors in recent years, however; if the neighborhood is to retain its identity it will require the combined efforts of local residents, the city government, and civic society organizations such as Historic Macon to carefully monitor and regulate future acquisitions. Demography of Beall’s Hill Despite these recent improvements in the neighborhood‟s physical infrastructure, many problems still remain. Low household earnings, low educational attainment, and low labor force participation rates are depressing property values and investment in the area, reinforcing a cycle of poverty due to a lack of sufficient tax base to fund public services. To better understand the population of the neighborhood, this study utilized the American Community Survey (ACS) to determine demographic characteristics at the census block group (CBG) level. The area defined as Beall‟s Hill, however, is composed of three different census block groups within Census Tract 137 of Macon-Bibb County (see Figure 1.3 and 1.4). Since the other two CBG‟s include the surrounding neighborhoods of Intown and Tattnall Heights, this study was only able to isolate demographic data for CBG 137.3. This area includes the majority of residential properties in Beall‟s Hill, however, as well as 31 of HMF‟s 38 property investments, making it a useful estimate of the demographic composition of the neighborhood.

Figure 2: Map of Area Surrounding Beall‟s Hill with Census Block Groups Outlined LandLink Analytic Services


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The following are demographic statistics for CBG 137.3, which are used as indicative of the neighborhood of Beall‟s Hill as a whole, taken from the 2014 5-Year ACS Estimate: Variable

2014 Estimate

Error Margin


2014 Estimate

Error Margin

Total Population


170 Households



White Population


123 Av. Household Size







White % of Population


Black Population Black % of Population


Med. Household Income 137 Households Earning < $10,000 Annually


$10k - $19,999



Population over 16


163 $40k - $49,999



… in Labor Force


54 $60k - $74,999



Population over 25


101 $75k - $99,999



Males over 25


106 Family Households



Females over 25




75 Nonfamily Households

% Pop over 25 with College Degree


Housing Units



% Pop over 25 with High School Diploma


Occupied Housing Units



% Pop over 25 w/out High School Diploma


Occupied Housing Units Below Poverty Level



Median Age


4.6 Owner Occupied Housing



Male Median Age


3.6 Renter Occupied Housing



Female Median Age


4.4 Median Contract Rent




$4,089 Vacant Housing Units









Per Capita Income Individuals


147 Median Number of Rooms

Individuals in Poverty


145 Median Property Value

Poverty Rate


Median Year Built

These statistics are estimates derived from the census, making the exact numbers questionable, but the outline they provide of the residents of Beall‟s Hill are critical. The relatively high poverty rate and rental rate make the neighborhood particularly sensitive to increases in the cost of housing; while current rental prices and property values indicate that long-term residents have not been displaced, this remains a danger for any development efforts. LandLink Analytic Services


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Review of Relevant Literature The fields of preservation and real estate economics both contain a wide variety of property value analysis studies, with an equally diverse set of opinions on the most appropriate methodology for measuring the impact of investments. The objectives of this research are focused on the intersection of these two fields, where the preservation of historic character – rather than profit - is the motivator for property acquisition, and where improving the quality of life for residents is more important than stimulating the citywide economy. In considering the best practices for balancing these quantitative and qualitative results, this study reviewed recent literature in both historic preservation and property valuation techniques. The recommendations of respected authors in these fields align more than initial expectations would suggest, and the resulting methodology of this study is informed by these commonalities. Need for Economic Impact Studies in Historic Preservation The authority on historic preservation in the United States is widely considered to be the Advisory Council on Historic Preservation (ACHP). This organization publicizes research on the effects of preservation and best practices, coordinating the multitude of nonprofit preservation organizations across the country. In a 2011 report to the ACHP at their annual meeting, Donovan Rypkema and Caroline Cheong of PlaceEconomics, a preservation impact assessment group from Washington DC, and Dr. Randall Mason of the University of Pennsylvania Historic Preservation Program, made the following observations: “The relationship between preservation and the economy… remains imperfectly understood and only partially documented. Research into the relationship between economics and historic preservation is critically needed,” (Rypkema, Cheong, and Mason, 2011). In recommending organizations within the ACHP network on how to conduct such economic impact studies, Rypkema et al emphasized that the most important criterion for assessment was utility. “There are multiple constituencies for this information, many of whom need the data and information presented in different forms. While the research and methodologies require scholarly robustness, the information needs to be presented in nonacademic terms,” (Rypkema, Cheong, and Mason, 2011). Traditional real estate economics has evolved such complex methodology and terminology as to be incomprehensible to most stakeholders. This prevents other organizations from replicating their studies, creating multiple LandLink Analytic Services


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standards of impact assessment or discouraging many from evaluating their economic impact at all. Rypkema et al argue that the end audience of assessment reports should be local government officials, and that “case studies need to be developed and shared so that their lessons can be applied locally and successful strategies replicated” (Rypkema, Cheong, and Mason, 2011). This focus on replication informs the primary criteria of this study. Property Value Assessment Methodology The basis of most economic impact studies for housing rehabilitation and preservation is assessment of its effect on property values. The importance of property values in a neighborhood is difficult to overstate; they determine housing prices and rental rates, therefore creating an indicator of the income level of residents and all of the socio-cultural associations that come with it. Property values also determine local tax revenues and thereby public service delivery, including municipal budgets for law enforcement and public schools. Improvements in property values, therefore, are closely linked to both increases in average income and neighborhood amenities. Measuring these changes is thus a foundational component of any assessment, but the methodology by which values are determined has created significant divides within the field. Rypkema et al discuss the primary techniques for valuing properties in their report: “Property values (and value changes) are measured in two alternative ways: actual transactions in the marketplace, or a proxy for those transactions. Since in most places in the United States, property taxes are levied on an ad valorum basis, the assessed value for taxation purposes can usually be effectively used as a proxy for sales prices. The advantages of using assessed valuation are: The numbers of properties are large, obviating the small sample problem that is encountered when using actual transactions. The assessed data is generally in the public record so can be easily accessed. Many jurisdictions have all of their property records computerized so sorting and evaluating becomes easier. Most of the variables between properties (size of lot, zoning, size of house, number of bathrooms, etc.) are usually included in the property records. Assessed value databases facilitate the use of GIS representation of findings.” (Rypkema, Cheong, and Mason, 2011). Clearly the usage of publicly assessed values is far easier than estimating values through infrequent transactions or any pricing methodology which attempts to determine values independently of existing records. Many studies utilize a strategy known as hedonic regression LandLink Analytic Services


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pricing, “in which the value of an individual property is seen as reflecting a bundle of values of individual amenities, which would include both characteristics of the property itself and characteristics of the neighborhood, and a regression equation is used to estimate the value of each individual amenity or property characteristic” (Rosen, 1974). Given the importance of replication and universal standards for revitalization and preservation groups, however, such a hedonic method would create too many non-standard variables to be useful. Rypkema et al support the use of assessed values on their statistical merit as well as their accessibility, arguing that: “it is not necessary that each value estimate is „right‟ as to the probable sales price tomorrow, as long as there is a consistent ratio between the market value and the assessed value for tax purposes” (Rypkema, Cheong, and Mason, 2011). Given these arguments, this study utilizes publicly assessed values as the basis for its methodology. Difference-in-Differences Regression Methodology The selection of assessed property values determines the dependent variable of an analysis, but the more complicated aspect of any study is the type of test used to observe the relationship between the intervention and property values. Most studies utilize some form of regression analysis to allow for statistical controls and significance testing, but there are dozens of variations in methodology, from the simplest bivariate ordinary least squares regression to highly complex models with hundreds of variables and interactions. This study aims to maintain statistical robustness while avoiding the need for trained economists to explain its results; as such it employs an easily understood technique known as difference-in-differences analysis. This methodology is supported by both traditional real estate economics and community development studies, primarily for its capacity to compare treatment and control groups within similar areas. A study on the effect of greening vacant lots by Megan Heckert of West Chester University describes the difference-in-differences approach as, “an econometric case-control test that investigates whether an intervention influences an outcome over time by comparing observed differences in a case sample that receives the intervention to observed differences in a control sample that does not. This approach enables isolation of the treatment effect above and beyond any difference that would have been expected regardless of the treatment (Meyer, 1995)” (Heckert, 2015). This provides greater support of causal relationships between variables than more simplified time series regressions, which may only support correlation between LandLink Analytic Services


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observations. By observing similar properties over the same time frame this approach can determine the effect of investments with controls for market-wide trends or other hedonic pricing factors, allowing an assessment of differences that is more accurate than simple means testing. Another study by Ioan Voicu and Vicki Been on the effect of community gardens supports the statistical accuracy of this method: “The difference-in-difference measure avoids having to compare properties near community gardens to other properties in different neighborhoods and accordingly avoids bias that might be introduced by any systematic differences between neighborhoods that host community gardens and other locations around the city” (Voicu and Been, 2008). While many studies attempt to create artificial representations of properties based on incomplete characteristics, difference-in-difference analyses allow comparisons with actual properties, reducing the likelihood of omitted variable bias and allowing results to be interpreted in real, rather than purely theoretical, terms. Spatial Dependence, Neighborhood Effects, and Time-Series Effects One of the key advantages of regression analyses like the difference-in-differences model is that they allow for control variables that remove the influence of confounding price determinants on the effect of the treatment on property values. The most important controls utilized across real estate economics studies are: spatial dependency, neighborhood effects, and time-series effects. This study includes all three controls in an attempt to standardize property value differences and allow for accurate comparison between treated and control observations. Spatial dependency is a geographic term referring to the relationships between properties which are proximal in location. Naanwaab utilizes the following definition in his spatial econometric analysis of subsidized housing on property values: “Spatial dependence is said to exist when there are spillover effects. Anselin (1988) defines spatial dependence as „the lack of independence in cross-sectional [spatial] observations.‟” These spillover effects complicate studies on the relationship between property investments and values, since improvements in one property may influence the value of properties nearby which did not receive investment, reducing the accuracy of the treatment and control group separation. Such spillover effects are well known in community development; in fact most revitalization strategies cite them as a positive externality of property investments. Since they represent a statistical complication in analysis and a potential variable of interest for developers, these spillover effects must be LandLink Analytic Services


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controlled for and measured in any property analysis. There are multiple methodologies by which spatial dependency may be measured; the particular technique employed by this study is explained in more detail later on. The basic form, however, is adapted from Heckert‟s study on greening vacant land, which she describes as follows: “The effect of proximity to an intervention on property values might be assessed by comparing property values at varying distances to the intervention and checking the coefficient of the distance variable to see if lower distances correspond to higher values” (Heckert, 2015). Geographic controls must measure more than proximity between properties to be effective, however. In determining the value of a particular property there are many features considered important by both public assessors and potential homebuyers: quality of local schools, proximity to amenities such as parks, distance to the central business district, perceptions of safety or social composition, etc. Naanwaab describes the importance of including such variables and their relationship with property values: “Research has shown that neighborhood factors like incomes and poverty status tend to be capitalized into house values” (Naanwaab, 2011). There are studies which attempt to capture all of these variables independently in a hedonic pricing model, but that method has already been determined overly complex for the objectives of this study. Many models instead utilize categorical neighborhood dummy variables to capture the effect of particular areas on property values. This approach is less nuanced but far easier to replicate, and allows regression models to standardize an entire city for comparison with single neighborhoods, increasing the number of observations dramatically and with it the statistical accuracy of the model. This is the approach taken by this study. The third most important category of control variables necessary for any study on property values is time effects. Regressions which utilize data from multiple years, an essential component of difference-in-difference analyses, must account for the changes in general market conditions over each of those years. This is particularly true when known distortions such as real-estate market fluctuations may affect property values; since this study encompasses years from 2008 to 2016 these time-series effects are critical for removing the influence of the housing market crash in 2008 and subsequent recovery. The studies by Heckert, Voicu and Been, and Naanwaab all utilize time-series effects by year as controls. Introducing fixed year effects as categorical factors rather than proxy variables for national average values is more accurate for

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this study, since they capture fluctuations particular for the city of Macon, which may have responded differently to market conditions than the rest of the nation. Best Practices in Community Revitalization The methodology of assessment is vital in determining the success of interventions after they occur, but most community revitalization organizations are more concerned with the viability of potential initiatives. As such it is also important to consider whether Historic Macon‟s work fits with the best practices of more general community revitalization groups. One study by Accordino, Galster and Tatlan describes a project that fits the basic strategy underlying Historic Macon‟s property initiatives: concentrating development investment in strategic low income areas to leverage private market action. According to the authors, broad public assistance strategies organized at the city level only ameliorate the effects of poverty in low income neighborhoods, alleviating some of the stresses of poor infrastructure and depressed property values without making any sustainable interventions. More localized strategies at the neighborhood level deploy resources more effectively and are more likely to build the critical mass necessary to create a turnaround for the area. Accordino et al. applied these principles to analyze the Neighborhoods in Bloom program of Richmond, Virginia, which concentrated Community Development Block Grant (CDBG) and Home Investment Partnership funds in seven key residential areas. The city funded subsidies for targeted property investments by various community development corporations to provide affordable financing for its target areas, ensuring low income residents have affordable access to housing. This partnership between local government and non-profit organizations caused sales prices in the program neighborhoods to rise by more than 20% compared to the citywide average between 1990 and 2003. Further analysis through an adjusted interrupted time series model demonstrated that there was an investment threshold of $20,100 per property blocks, below which effects were negligible but beyond which home prices increased by 50% over the study timeframe. In the conclusion of their study, Accordino et al. suggest that, “the public and nonprofit sectors should target their resources so as to achieve a threshold level beyond which the private market can operate without subsidies (except where they are needed to maintain affordability or to preserve historic structures)” (Accordino et al. 2005). These ideas are guiding principles for LandLink Analytic Services


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Historic Macon‟s work in property development, demonstrating that their strategies are supported by similar nonprofit community revitalization efforts. As such, this assessment of their work may be applied to more general residential development efforts as well as those focused specifically on preservation.

Research Hypothesis and Framework The hypothesis of this study is that the Historic Macon Foundation‟s involvement in property acquisition and development has had a significant impact on the property values of Beall‟s Hill between the years 2008 and 2016. The null hypothesis for this study is that no observable relationship exists between Historic Macon‟s involvement in a property and its value difference from the neighborhood average; failing to reject this null would indicate that HMF has had no impact and changes in value are due to other factors. If the null hypothesis is rejected, however, it would indicate that HMF‟s investments have made a difference in property values. Considering that many of HMF‟s acquisitions are of dilapidated or empty lots and its sales are of market rate residential housing, it is likely that an effect will be observed. The true interest of this study is the magnitude of the change and how it compares with the value trend for the rest of the neighborhood, as well as the potential spillover effect of these investments on surrounding properties. The dependent variable in this study will be based on assessed property values between 2008 and 2016, while the independent variable will be a categorical factor of HMF‟s involvement in the property. The causal mechanism linking these two variables is the investments in redevelopment that Historic Macon has made in the properties.

Data Sources and Methodology of Analysis The data used in this research was acquired from three sources: demographic and socioeconomic data on Beall‟s Hill from the American Community Survey in the US Census, public property value and sales data from the Macon-Bibb County Tax Assessor‟s Office, and property development and investment data from the Historic Macon Foundation. The first two are publicly available for any location in the United States, and while property records are formatted differently by state the standard categories of information utilized by this study should be consistent for any county or municipal office with electronic records.

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Organization of Data for Analysis The property data utilized in this analysis was organized in a spreadsheet, converting the databases provided by the tax assessor into a format understandable by statistical software such as R for regression analysis. The primary change required was a shift from separate columns for each year of property values to a single value column, with repeated property observations representing different years. Each property, represented by the unique identifier Map_Route, was accompanied by tax classification (residential, commercial, tax-exempt, etc.) and parcel size in acres. These were held as constant using their 2016 status over the study time series; while some properties may have changed classification or altered in size through splits or combinations there was not enough information available on these changes to apply them across the entire city. Only residential properties were considered in this study, since the inclusion of commercial and taxexempt properties in the analysis was found to mask the actual changes in the residential property market; since this study is primarily concerned with the value of houses in a residential district, this subset of the data is still considered accurate. Additional identifiers were added to each observation representing the propertyâ€&#x;s status as having been acquired by Historic Macon or not for each year, as well as a categorical factor for the neighborhood in which each property was located. These were determined using Historic Maconâ€&#x;s neighborhood boundaries wherever possible; for some neighborhoods no accepted boundaries exist, and natural geographic borders such as highways and rivers were used instead. The process of assigning neighborhood classification to each property was conducted using parcel selection in a geographic information system software; neighborhood boundaries were drawn and each property within assigned with the neighborhood title. These were translated into categorical factors for each neighborhood so that they could be expressed as dummy variables for controls in each regression. A sample of this spreadsheet can be found in Appendix A. Spatial Dependency Calculations using GIS The process of calculating the spatial dependency variable, which is used to measure the spillover value of Historic Maconâ€&#x;s property investment, was the most technically complicated portion of this study. To determine the effect of Historic Macon investment on surrounding property values, the centroid coordinates of each property in the city of Macon were measured by their distance in meters from every property that had been acquired by HMF in that year or LandLink Analytic Services


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previous years. The distances from each property centroid to the centroids of every Historic Macon property were added to determine an aggregate distance, expressed as the variable Sum of Distance from HMF. To determine the centroid coordinates of each property, a shapefile of property geography was acquired from the city tax assessor and opened using an open-source geographic information system software, QGIS. The software allowed the extraction of parcel centroids, which are the exact center point of each property as expressed in latitude and longitude, from the property map. The distance between each property‟s centroid and those of every HMF property was then measured using a geographic package in the statistical software, R, which is also opensource. The packages used are titled “geosphere” and “readr”; these allow a matrix of all longitude and latitude coordinates for each property to be created using the “matrix” command. The distances between each coordinate on the matrix can then be measured using the “distm” command. Each distance can be added together to find the aggregated distance using the “rowSums” command. These distances are then added back to the original spreadsheet of property data. This process had to be repeated for each year, since the number of properties which had received HMF treatment changed every year. The RStudio script for running this analysis can be found in Appendix B for replication. Regression Methodology Overview The method of analysis utilized by this study is a multivariate regression, which measures the effect of changes in an independent variable on the dependent variable while controlling for the influence of potentially confounding variables. Regressions provide insights on patterns in data, making them particularly useful for determining trends over time. The difference-indifferences test employed by this study compares the trend of a treated group, such as a set of properties which received investment, and an untreated group of all other properties in an area. This comparison quantifies the difference in the dependent variable, in this case property values, between the treated and untreated properties. These differences demonstrate the magnitude of impact which the treatment has had on the value of properties. By controlling for other factors which may influence a property‟s value, the regression can indicate what the expected impact of treatment will be in a generalized case, without regard for the year, size, or relative proximity to

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other treated properties. The regression also indicates how accurate these predictions are through statistical significance testing, providing a confidence level of the prediction. Explanation of Variables The variables included in this analysis fall into three categories: the dependent variable, independent variable of interest, and control variables. There are two dependent variable used in the regressions. The first, which is used by regression A, is the assessed value of residential Property i in the study area at Year t, expressed by the variable Residential_Valueitn. The second, which is used by regressions B – F, is the change in value from Year t-1 to Year t, expressed as Change_in_ResidentialValueitn. The designation of n indicates which neighborhood the property is categorized within. There are also two different independent variables used in the regressions, though both are categorical factors which indicate whether a property received the treatment of acquisition by HMF. The first, used by regressions A and B, is the status of Property i in Year t as having been acquired in that year or any previous years by HMF, expressed by the variable Has_Been_HMFit. The second, used by regressions C – F, is a lag of treatment indicating the number of years since Property i was acquired by HMF at Year t, expressed by the variable HMF_Acquired_N_Years_Agoit, in which N represents the number of years since acquisition. In 2008, which is the earliest year of the study, HMF had acquired 5 properties, but by 2016 there were 38 properties in Beall‟s Hill which had been acquired by the Historic Macon Foundation. The control variables used in the regressions are based on the sum of distances between the property and HMF properties, the acreage of the property, year fixed effects, and a categorical variable of the neighborhood the property is in. The sum of distances between Property i and all Historic Macon properties at Year t is expressed by the variable Sum_Distance_from_HMFit. The acreage of Property i, which is kept constant at the 2016 value over each year, is expressed by the variable Acresi. The fixed effects of each year, which are used to control for the impact of the annual market wide fluctuations, are determined as factors of each year from 2008 to 2016 and expressed by the variable Yeart. The neighborhood of Property i is used to control for the effect of each locality within the city of Macon – 35 neighborhoods in total excluding Beall‟s Hill – and is expressed by the variable Neighborhoodn. The regressions do not include a dummy variable for the neighborhood of Beall‟s Hill to prevent multiLandLink Analytic Services


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collinearity between the categories; the omission of Beall‟s Hill makes that neighborhood the reference category for the intercept coefficient. The combination of this neighborhood reference and the other control variables effectively creates an estimate of what the property would be without the influence of those factors on its value. By holding the effects of differences in year, neighborhood, acreage, and distance from a Historic Macon property as constant the only variable which is allowed to change is the status of having been acquired by Historic Macon or not. This isolation of the treatment effect is critical for the measurement of the actual impact of Historic Macon; without the controls, the differences in value may be attributable to other market trends, reducing the statistical significance of the analysis and weakening the argument for the rejection of the null hypothesis. Difference-in-Difference Regression Formulas Three categories of regression were utilized in the analysis: Regression A observes the effect of a property‟s acquisition at any point by Historic Macon on the assessed value of residential properties, Regression B observes the same treatment on the change in value by year, and Regressions C – F observe the effect of acquisition with different year lag effects on the change in value for that year. Each regression was run on a set of property observations at three geographic levels: across the entire city of Macon, a selection of neighborhoods surrounding Beall‟s Hill (Huegenin Heights, Tattnall Square Heights, Intown, Tindall Heights, Pleasant Hill, and Montpelier Heights) and including Beall‟s Hill, and just Beall‟s Hill itself. This results in 18 total regressions, which may be compared to observe the effects of the variations in methodology. The three categories of regression were employed to determine whether acquisition by Historic Macon has a noticeable effect on the actual property values per year or the change in value from one year to the next; the lag year effects were introduced to determine when the treatment effect is realized by an appreciation of assessed property values. The changes in geographic level were employed to determine whether the effect of acquisition is significant in comparison to the entire city, the surrounding residential neighborhoods, or within the neighborhood of Beall‟s Hill itself; since neighborhood effects are controlled for the treatment effect itself would not be expected to change much, but the variable measuring the spillover effect of spatial dependency may differ depending on the scope of the study. LandLink Analytic Services


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The formulas utilized to run each category of regression are given below: Regression A Formula: Residential_Valueitn = B0 + B1*Has_Been_HMFit + B2*Sum_Distance_from_HMFit + Bt*Yeart + B4*Acresi + Bn*Neighborhoodn + Eitn Regression B Formula: Change_in_ResidentialValueitn = B0 + B1*Has_Been_HMFit + B2*Sum_Distance_from_HMFit + Bt*Yeart + B4*Acresi + Bn*Neighborhoodn + Eitn Regression C – F Formula: Change_in_ResidentialValueitn = B0 + B1*HMF_Acquired_N_Years_Agoit + B2*Sum_Distance_from_HMFit + Bt*Yeart + B4*Acresi + Bn*Neighborhoodn + Eitn

Research Results and Interpretation The results of the regression analysis are presented by the coefficient corresponding to the variable being measured in the formula. Since the dependent variable in each regression is expressed in dollars, the coefficients will all be in dollar terms; the meaning of each differs depending on the variables being measured, whether the amounts refer directly to assessed property values or the annual change in assessed values. The spillover effect measured by the coefficient of the aggregated distance from HMF properties is expressed in the change in dollar value of a property per meter from all properties that have been acquired; a positive number indicates that property values increase with distance, while a negative number indicates that values increase with proximity to HMF properties. The standard error of the coefficient is a measure of the accuracy of the estimate; higher errors relative to the coefficient mean that the prediction may vary more widely from the expected value. The t-value and Pr(>|t|) value are measures of statistical significance testing whether the estimate rejects the null hypothesis; larger t-values and smaller p-values mean the estimate is more significant and therefore a more accurate predictor of the coefficient value. The asterisks in the significance level column indicate the confidence level as given by the legend at the bottom of the results chart. If there are no asterisks it indicates that the estimate is not statistically significant enough to reject the null hypothesis, and thus cannot be used to explain the dependent variable, property values.

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Results of Regression 1 – Citywide Residential Property Values Regression 1a: Residential Property Value on Has Been HMF

Coefficient Estimate

Standard Error


Pr (>|t|)

Signif. Level *** ***

B0 : (Intercept) $58,220.00 $1,029.00 56.568 2.00E-16 B1: ResidentialValue$Has_Been_HMF $29,010.00 $6,206.00 4.675 2.94E-06 B2: ResidentialValue$Sum_Distance_from_HMF $0.005 $0.00 1.535 0.124794 Regression 1b: Change in Residential Property Value on Has Been HMF B0 : (Intercept) $13,220.00 $280.50 47.135 2.00E-16 *** B1: ResidentialChange$Has_Been_HMF $12,780.00 $1,697.00 7.532 5.03E-14 *** B2: ResidentialChange$Sum_Distance_from_HMF -$0.020 $0.00 -19.7 2.00E-16 *** Regression 1c: Change in Residential Property Value on Acquired by HMF One Year Ago B0 : (Intercept) $13,350.00 $278.30 47.981 2.00E-16 *** B1: ResidentialChange$HMF_Acquired_1_Year_Ago $4,207.00 10.477 2.00E-16 *** $44,080.00 B2: ResidentialChange$Sum_Distance_from_HMF -$0.020 $0.00 -19.806 2.00E-16 *** Regression 1d: Change in Residential Property Value on Acquired by HMF Two Years Ago B0 : (Intercept) $13,430.00 $278.30 48.25 2.00E-16 *** B1: ResidentialChange$HMF_Acquired_2_Years_Ago $4,208.00 5.858 4.68E-09 *** $24,650.00 B2: ResidentialChange$Sum_Distance_from_HMF -$0.020 $0.00 -19.763 2.00E-16 *** Regression 1e: Change in Residential Property Value on Acquired by HMF Three Years Ago B0 : (Intercept) $13,480.00 $278.30 48.432 2.00E-16 *** B1: ResidentialChange$HMF_Acquired_3_Years_Ago $9,167.00 $4,030.00 2.275 0.0229 ** B2: ResidentialChange$Sum_Distance_from_HMF -$0.020 $0.00 -19.775 2.00E-16 *** Regression 1f: Change in Residential Property Value on Acquired by HMF Four Years Ago B0 : (Intercept) $13,470.00 $278.30 48.424 2.00E-16 *** B1: ResidentialChange$HMF_Acquired_4_Years_Ago $11,570.00 $4,208.00 2.75 0.00596 *** B2: ResidentialChange$Sum_Distance_from_HMF -$0.020 $0.00 -19.767 2.00E-16 *** Significance Level Codes: *** : 0.001 ** : 0.01 * : 0.05 • : 0.01 None : <0.01

The results of the first regression indicates that, controlling for the size, neighborhood, and year, HMF acquired properties have a lower average value than the average residential properties in Bealls‟ Hill. The intercept coefficient, given by B0, provides a measure of property values for an estimated average property in Beall‟s Hill of average size in an average year; it is a model property creating by removing the effect of all control variables on property value, and by leaving the category of Beall‟s Hill out of the neighborhood controls the intercept provides an average for properties in that neighborhood. The independent variable coefficient, given by B1, is an estimate of the average value of an HMF treated property with similar controls for year and size. That the average HMF value is lower than the Beall‟s Hill average is not surprising, considering that HMF intentionally selects blighted or vacant properties for acquisition, so their LandLink Analytic Services


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initial values are not likely to be higher than the residential average for the area. The measure of assessed value only accounts for average property values, not increases or decreases per property, and by considering all properties which have received treatment the regression includes properties on their year of acquisition before any investment is made, which is likely lowering their estimated values further. That is why further regressions are required to account for the change in value per year and changes given number of years since acquisition. The results of Regression 1b indicate that the change in value for properties which have received HMF treatment are slightly lower than the average in Beall‟s Hill. The results of the following regressions, which separate the effect of acquisition by year, indicate that there is a significant increase in the annual appreciation of a property the first and second year after acquisition, after which the increase in value is below that of average properties. This sharp increase followed by a slightly lower increase may explain why the average change in value from 1b is lower; HMF properties seem to appreciate at a lower rate once they have settled on a higher value, indicating that their value is less volatile after the effects of HMF investment are accounted for. This may be due to an overvaluation in response to the initial investment or an effect of the covenants placed on Historic Macon homes after they are resold, requiring that owners maintain their historic character and prevent them from sub-leasing or re-selling the property without the approval of HMF. The lower increases in Regressions 1e and 1f may also indicate that property owners are not re-selling their HMF improved homes, and therefore they are not receiving re-assessment by the city. The distance to HMF properties, given by the coefficient B2, is not a significant factor in the value of residential properties across the city, but it is significant and negative on the change in a property‟s value for Regressions 1b through 1f, indicating that as properties become closer to Beall‟s Hill their value increases more per year than those more distant, in the estimated amount of two cents per meter. This is likely due more to uncaptured effects of a property‟s location in the city, since Beall‟s Hill is located near to the urban core and public amenities such as Tattnall Square Park it is likely that residential properties closer to the neighborhood as a whole experience higher annual appreciation. The spillover effect will not likely become significant unless the study area is restricted to Beall‟s Hill, since an HMF investment will not likely alter the value of a property on the other side of the city.

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Results of Regression 2 – Surrounding Neighborhoods Property Values Coefficient Standard t-value Pr (>|t|) Signif. Regression 2a: Residential Property Value Surrounding Estimate Error Level Beall’s Hill on Has Been HMF B0 : (Intercept) $43,820.00 $1,249.00 35.082 2.00E-16 *** B1: SurBHResValue$Has_Been_HMF $26,250.00 $5,620.00 4.67 3.02E-06 *** B2: SurBHResValue$Sum_Distance_from_HMF $0.240 $0.03 8.248 2.00E-16 *** Regression 2b: Change in Residential Property Value Surrounding Beall’s Hill on Has Been HMF B0 : (Intercept) $13,170.00 $334.10 39.413 2.00E-16 *** B1: SurBHResChange$Has_Been_HMF $13,080.00 $1,545.00 8.464 2.00E-16 *** B2: SurBHResChange$Sum_Distance_from_HMF $0.025 $0.01 3.105 0.0019 *** Regression 2c: Change in Residential Property Value Surrounding Beall’s Hill on Acquired by HMF One Year Ago B0 : (Intercept) $13,260.00 $332.90 39.815 2.00E-16 *** B1: SurBHResChange$HMF_Acquired_1_Year_Ago $3,825.00 11.416 2.00E-16 *** $43,660.00 B2: SurBHResChange$Sum_Distance_from_HMF $0.022 $0.01 2.797 0.00516 *** Regression 2d: Change in Residential Property Value Surrounding Beall’s Hill on Acquired by HMF Two Years Ago B0 : (Intercept) $13,340.00 $333.30 40.008 2.00E-16 *** B1: SurBHResChange$HMF_Acquired_2_Years_Ago $3,830.00 6.204 5.56E-10 *** $23,760.00 B2: SurBHResChange$Sum_Distance_from_HMF $0.023 $0.01 2.868 0.00413 *** Regression 2e: Change in Residential Property Value Surrounding Beall’s Hill on Acquired by HMF Three Years Ago B0 : (Intercept) $13,370.00 $333.50 40.092 2.00E-16 *** B1: SurBHResChange$HMF_Acquired_3_Years_Ago $10,080.00 $3,669.00 2.748 0.006 *** B2: SurBHResChange$Sum_Distance_from_HMF $0.022 $0.01 2.822 0.00478 *** Regression 2f: Change in Residential Property Value Surrounding Beall’s Hill on Acquired by HMF Four Years Ago B0 : (Intercept) $13,370.00 $333.40 40.106 2.00E-16 *** B1: SurBHResChange$HMF_Acquired_4_Years_Ago $12,210.00 $3,832.00 3.185 0.00145 *** B2: SurBHResChange$Sum_Distance_from_HMF $0.023 $0.01 2.845 0.00444 *** Significance Level Codes: *** : 0.001 ** : 0.01 * : 0.05 • : 0.01 None : <0.01

The results of Regression 2a reinforce the findings of the citywide study; considering only those properties in neighborhoods surrounding Beall‟s Hill, after the same controls are used to generalize the intercept to an average Beall‟s Hill property, the average value of an HMF treated property is still less than the estimated average property. In this study the distance from HMF properties is significant and positive for all regressions, indicating that values decrease as properties come closer to Beall‟s Hill; this is not surprising, since most of the surrounding neighborhoods – such as Intown and Tattnall Square Heights – are higher in value and closer to the central business district than Beall‟s Hill and thereby Historic Macon properties.

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The change in the values of properties are almost identical for those receiving HMF treatment and those not, once again indicating that the annual appreciation of a treated property is similar to the average. Considering Historic Macon acquires empty lots and blighted houses for revitalization, it should actually be considered an accomplishment that their houses follow the local average for residential properties which were already being lived in. The year lag change in value regressions indicate similar results to the citywide regressions, demonstrating a sharp increase in the average value of a property for the first two years followed by a slight dip before settling close to the intercept value. Regression 3 – Beall’s Hill Property Values Coefficient Standard t-value Pr (>|t|) Signif. Regression 3a: Residential Property Value in Beall’s Hill on Estimate Error Level Has Been HMF B0 : (Intercept) $2,036.00 $3,630.00 0.561 0.575 B1: BHResValue$Has_Been_HMF $6,914.00 5.328 1.06E-07 *** $36,840.00 B2: BHResValue$Sum_Distance_from_HMF $0.44 13.958 2.00E-16 *** $6.094 Regression 3b: Change in Residential Property Value in Beall’s Hill on Has Been HMF B0 : (Intercept) $13,900.00 $1,110.00 12.524 2.00E-16 *** B1: BHResChange$Has_Been_HMF $12,030.00 $2,186.00 5.50 4.05E-08 *** B2: BHResChange$Sum_Distance_from_HMF $0.13 -2.128 0.033426 ** -$0.277 Regression 3c: Change in Residential Property Value in Beall’s Hill on Acquired by HMF One Year Ago B0 : (Intercept) $13,960.00 $1,103.00 12.654 2.00E-16 *** B1: BHResChange$HMF_Acquired_1_Year_Ago $5,286.00 8.137 5.96E-16 *** $43,010.00 B2: BHResChange$Sum_Distance_from_HMF $0.13 -2.823 0.004786 *** -$0.358 Regression 3d: Change in Residential Property Value in Beall’s Hill on Acquired by HMF Two Years Ago B0 : (Intercept) $13,950.00 $1,112.00 12.536 2.00E-16 *** B1: BHResChange$HMF_Acquired_2_Years_Ago $5,338.00 4.113 4.01E-05 *** $21,960.00 B2: BHResChange$Sum_Distance_from_HMF $0.13 -2.989 0.00282 *** -$0.383 Regression 3e: Change in Residential Property Value in Beall’s Hill on Acquired by HMF Three Years Ago B0 : (Intercept) $13,940.00 $1,115.00 12.498 2.00E-16 *** B1: BHResChange$HMF_Acquired_3_Years_Ago $7,920.00 $5,122.00 1.546 0.12217 B2: BHResChange$Sum_Distance_from_HMF $0.13 -3.207 0.00136 *** -$0.412 Regression 3f: Change in Residential Property Value in Beall’s Hill on Acquired by HMF Four Years Ago B0 : (Intercept) $13,960.00 $1,115.00 12.517 2.00E-16 *** B1: BHResChange$HMF_Acquired_4_Years_Ago $10,220.00 $5,356.00 1.908 0.05652 * B2: BHResChange$Sum_Distance_from_HMF $0.13 -3.154 0.00163 *** -$0.405 Significance Level Codes: *** : 0.001 ** : 0.01 * : 0.05 • : 0.01 None : <0.01

The results of the last set of regressions, with the dataset restricted to residential properties within Beall‟s Hill only, offer the most interesting but least statistically significant set LandLink Analytic Services


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of coefficient values. Regression 3a shows a much higher average value for HMF treated properties than previous regressions, but the intercept lacks significance, making it difficult to compare values across estimates. The distance variable is significant and much higher than previous regressions, at $6.094 per meter away, indicating that every meter in additional distance a property is from the aggregated combination of HMF properties the value increases by over $6. This is likely due to HMF properties being concentrated in the southern portion of Beall‟s Hill, which is systemically lower in value than the northern portion. The effect of HMF acquisition on the change in value, given by Regression 3b, follows the pattern of the first two regressions, indicating that HMF properties appreciate in value slightly less than other properties in Beall‟s Hill. The similarity of Regression b across all geographic contexts indicates that the neighborhood control variables were effective in isolating factors determined by location. The distance to HMF coefficient is negative and significant at a 99% confidence level, indicating that properties closer to HMF acquisitions increase in value more than those further away. This shows that while further properties had higher overall values, the rate at which those values increase is lower than those properties closer to HMF acquisitions; this is logical given HMF is targeting work in a lower valued portion of the neighborhood but encouraging since it shows that there is a positive spillover effect of their investments on the value change in surrounding properties. Regressions 3c – 3f show a similar pattern to previous study areas, indicating a large appreciation in value for the first two years followed by a slower increase in subsequent years. This again supports the findings of previous regressions that HMF acquisition improves the value of a property but does not cause it to continue increasing indefinitely, an effect which would distort the local property market and lead to issues of spiraling gentrification. While the third and fourth year from acquisition estimates are only significant at the 87% and 94% confidence level respectively, the similarity in pattern to previous regressions would indicate that these results are reliable; the decreased significance is likely due to the decrease in the number of properties being considered. The distance coefficient measuring the effect of proximity to HMF acquisitions on the change in value of a property becomes greater in the negative direction for Regressions 3c – 3f. This indicates that there is a significant and positive spillover effect on nearby properties, and it may increase in magnitude over time. The increase may also be due to changes in value not LandLink Analytic Services


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captured by the independent variable of HMF treatment, since it is not significant in Regressions 3e and 3f, so the appreciation of nearby properties is being captured by the distance coefficient. Even if the restricted data size confounds some of the results, there is clearly a positive direct relationship between the proximity of a property to Historic Macon properties and its appreciation in value. Mapping Results through GIS The regression analyses provide a quantitative estimate of the effect of Historic Macon acquisitions on property values, but they do not capture the effect of their involvement on individual properties within the neighborhood. In order to better understand the relationship between HMF‟s involvement in Beall‟s Hill and the changes in property values over time, an open-source geographic information systems software – QGIS – was utilized to map the values of each property. Darker blue properties correspond with relatively higher values, while lighter properties have lower values. Light green properties indicate those which have been acquired by HMF in the past, while yellow properties were acquired in the year presented. The pink outline represents the boundaries of Beall‟s Hill, while other neighborhoods in which HMF has worked are red and yellow.

Figure 3: Property Values for Beall‟s Hill and Surrounding Area in 2008

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As explained previously, the area in which HMF has focused their acquisitions is on the southwestern quadrant of Beall‟s Hill; while it is higher in value than the southeast quarter, it is lower than the northern portion of the neighborhood. The large dark blue property immediately north of the HMF properties is the Oglethorpe Homes public housing block, while the dark blue property to its west represents a local middle school, Alexander II. The large blue property to the west of Beall‟s Hill is Mercer University, while the property immediately north surrounded by HMF neighborhoods is Tattnall Square Park. HMF‟s strategy has always been to concentrate property investments in a single area to maximize the stabilizing effect of revitalization on a block. The four previous acquisitions, made in 2006 and 2007, were all along the same street, while the newest property acquired in 2008 ventured further to the south, attempting to expand HMF‟s influence into less stable sections of the neighborhood.

Figure 4: Property Values for Beall‟s Hill and Surrounding Area in 2012 By 2012, HMF had acquired 21 new properties, 7 of those in 2012. The focus of their work remained on the southwestern portion of the neighborhood, with a few targeted acquisitions on the northeastern side. The scale of acquisitions and rehabilitation projects increased dramatically during this period, when the College Hill Alliance began working in partnership with Mercer University to create a sense of cultural identity in the area and connection with the downtown district. It is important to observe the change in value of properties in the blocks LandLink Analytic Services


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which HMF had previously acquired properties, represented by their color shifting from lighter to darker blue. This effect is difficult to notice when observing values year by year, but comparing the shading of properties from 2008 to 2016 yields dramatic differences.

Figure 5: Property Values for Beallâ€&#x;s Hill and Surrounding Area in 2016 By 2016 the southwestern quadrant of Beallâ€&#x;s Hill appears similar in value to the northern section, indicating a significant increase in the value of all properties, not just those treated by Historic Macon. This supports the findings of the regression analysis of the spillover effect raising the annual change in value of properties for those closer to HMF treated parcels. It is also important to note that HMF has expanded their work across the entire south side of the neighborhood by this point, with only a few blocks on the far southeast without any HMF involvement. If the effects of acquisition and investment remain similar for future HMF projects, it can be expected that this area will also increase in value to create a more economically stable neighborhood, improving the prospects for all homeowners in the area.

Conclusions for Stakeholders and Preservation Economics Field The objective of this study was twofold: to determine the impact of the Historic Macon Foundation on the neighborhood of Beallâ€&#x;s Hill, and to create a replicable methodology of localized impact assessment for similar community revitalization organizations, particularly those doing similar work in historic preservation. The difference-in-differences regression analyses demonstrated that HMF has had both a direct impact on the value of the properties LandLink Analytic Services


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which it acquired, increasing the value of its properties by an estimated $43,000 in the first year and $23,000 in the second, and a positive spillover effect on the surrounding properties in Beall‟s Hill. The GIS maps created of relative property values in the neighborhood confirm this effect visually, demonstrating that the entire section of Beall‟s Hill in which HMF has concentrated their efforts has seen an increase in value. The scale of this impact was gradual, however, and even those properties receiving direct investment stabilized to meet the average value change in the area, supporting Historic Macon‟s position that their work has not contributed to significant displacement of residents or other gentrifying effects in the area. Substantial value increases were only observed in HMF‟s target blocks, as the southeastern quadrant of Beall‟s Hill remained close to 2008 values in 2016, indicating that their work has not drawn speculative investors to purchase properties in the area for resale. This supports the strategy of Historic Macon to encourage gradual change in property values to stabilize the neighborhood without tipping it into uncontrolled development. Given the high rate of poverty and proportion of residents renting their housing, this gradual strategy is an effective deterrent to the negative externalities of development. The pace of acquisitions also allows residents to adjust to new homeowners, preventing the sense of “invasion by outsiders” that would create social divides in the area. Historic Macon‟s commitment to social integration within the community has also contributed to the neighborhood‟s cohesion; by hosting local socials, creating community spaces, encouraging cycling and walking, and maintaining a personal connection to the neighborhood, HMF has maintained their mission of preserving the cultural character of Beall‟s Hill while simultaneously reducing the number of blighted and vacant properties. The conclusion of this study for Historic Macon, therefore, is to encourage continuation of their selective, gradual strategy of development, and to suggest further expansion into the southwestern quadrant of Beall‟s Hill which still suffers from systemic undervaluation of their properties. Regular economic impact studies measuring property value changes should be conducted to ensure that development does not tip into speculative investment; while changes in methodology to improve accuracy may be advisable, the variables and geographic scope of future assessments should follow the model used in this study to allow for relevant comparison of data and results. The conclusions of this study for similar community revitalization organizations would encourage them to follow the Historic Macon model of investing not only in the economic assets of their neighborhoods but paying close attention to the social capital present. All organizations LandLink Analytic Services


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should conduct regular economic impact studies to determine the effects of their work on the property values of their target areas, since these are often an effective measure of the affordability and desirability of an area for residents. Impact studies should follow a regular methodology so that results may be compared across time and with similar initiatives by other organizations. Assessed property values should continue to be utilized as the standard for evaluation, since they are publicly available and follow similar methodologies across most municipal areas in the United States. Whenever possible both data and results should be shared with nationwide networks, such as the Advisory Council on Historic Preservation (ACHP), so that the authenticity of the study can be confirmed and methodology made available to other organizations. Impact assessment should not be viewed as a competitive advantage for funding or donor support, but rather a regular metric by which the organization guides its decisions; as such it should always be shared publicly and made available to other community organizations. As more organizations are being held to quantifiable results, standards of assessment should be developed and disseminated as best practices so that the entire field of development and community revitalization may gain more credibility by their use. The emerging field of preservation economics would also benefit from the distribution of assessment methodology; encouraging replication and verification of results is the cornerstone of scientific research, so more studies should be published with complete methodologies rather than held as proprietary consultation tools. As Rypkema et al concluded in their report to the ACHP, universal standards of assessment are necessary for the replication of impact studies across the nation and around the world. If historic preservation is to be taken seriously as an economic development tool it must embrace more scientific and quantitative approaches, particularly as more preservation organizations turn to investment and spatial planning as means of preserving community heritage. This professionalization must remain balanced, however, to prevent the shift of preservation into academic jargon, since the diverse range of public stakeholders necessary for preservation work requires that reports remain comprehensible. While it may be tempting to simply hire professional economists and consultants to conduct statistical studies on an as-needed basis, it would be far better in the long-term for preservation organizations to ensure their own staff are well versed in the basics of econometrics and analysis, including the use of statistical and GIS software. As more and more data becomes available for the assessment of community impact, the field of preservation economics must ensure that its work keeps up LandLink Analytic Services


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with the methodology of more mainstream economic fields, lest the vital work of preservationists be supplanted by less scrupulous community organizers with fewer concerns for the historic heritage and cultural significance of public spaces. Likewise, preservationists must be willing to step away from traditional techniques, such as house museums and walking tours, and embrace more innovative strategies of community revitalization, such as property investments and network building, or else their own work may be relegated to the annals of history.

Bibliography Accordino, John, George Galster, and Peter Tatian. "The Impacts of Targeted Public and Nonprofit Investment on Neighborhood Development: Research Based on Richmond, Virginia's Neighborhoods in Bloom Program." (2005): n. pag. Web. 5 Dec. 2016. Anselin, L. (1988). “Spatial Econometrics: Methods and Models.” Studies in Operational Regional Science. Kluwer Academic Publishers. Heckert, Megan. 2015. “A Spatial Difference-in-Differences Approach To Studying the Effect of Greening Vacant Land on Property Values,” Cityscape: A Journal of Policy Development and Research 17 (1): 51 – 59. Meyer, Bruce D. 1995. “Natural and Quasi-Experiments in Economics,” Journal of Business and Economic Statistics 13 (2): 151–161. Naanwaab, Cephas Banlenan. "A Spatial Econometric Analysis of the Effects of Subsidized Housing and Urban Sprawl on Property Values." Doctoral Dissertation to Graduate Faculty of Auburn University (2011): n. pag. Web. 5 Dec. 2016. Rosen, Sherwin. 1974. “Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition,” Journal of Political Economy 82 (1): 34–55. Rypkema, Donovan, Caroline Cheong, and Randall Mason. "Measuring Economic Impacts of Historic Preservation: A Report to the Advisory Council on Historic Preservation." (2011): n. pag. Web. 5 Dec. 2016. Voicu, Ioan and Been, Vicki. 2008. “The Effect of Community Gardens on Neighboring Property Values.” Real Estate Economics 36 (2): 241 – 283.

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