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Effects of Grocery Store Openings and Closings on Durham Housing Prices by Li Ding

Introduction and Background

Extending my previous work, this paper explores the effects of opening and closing food retail stores on nearby housing prices in Durham, North Carolina. While the opening of a new grocer provides nearby residents with increased access to food and other goods, the full economic impacts of new stores are not always clear. In particular, there is often resistance to larger retail stores like Walmart being opened in a neighborhood, with arguments that a new supercenter will negatively affect local businesses and wage levels. Additionally, increased traffic, crime, noise pollution, and so on are also potential concerns regarding new stores. As such, it is not apparent how, if at all, new stores affect nearby home prices. Changes in housing prices can be used as a signal to evaluate these various competing effects and so the results of this study will help Durham residents and government officials evaluate whether to support or oppose new stores in their local areas.

Pope and Pope (2014) examined the effects of opening a Walmart on nearby home prices from 2000 to 2006, and this paper will use a similar framework to their study. Using a difference-in-differences specification, the authors found that houses located within 0.5 miles of a new Walmart saw an increase of 2-3 percent in sale price in the following two and a half years, and houses 0.5 to 1 mile away saw a 1-2 percent increase. These results would suggest a positive impact for new stores in Durham as well, though the authors are careful to note that their results only reflect a national average.

Publicly available home sale data for Durham extends ten years back (to 2005), so this study will analyze store openings and closings that have occurred in that time frame. This includes Walmart, Harris Teeter, TROSA Grocery, ALDI, and Save-a-lot stores. Stores like Target and Food Lion are not included in the study because their newest stores were opened 2004 or earlier.

Housing Data

This paper analyzes data provided by the Durham County Tax Administration on home sales in Durham, NC. Through the county’s online record search, I collected the electronic summary record of each single-family residential home sale in Durham since 2005, the earliest year for which data is provided. These searches yielded approximately 27,000 results, and an Excel VBA macro was used to access the details page for each sale and record characteristics of the property (bedrooms, bathrooms, and so on). This data were filtered to remove a small number—around one percent—of entries with incomplete information.

Next, I used the Rest-CSV interface provided through Geocoder.us to find the longitude and latitude of the homes in the dataset. A macro was written to query the Geocoder.us servers and record the geographic coordinates for the homes included in the service’s dataset. Geocoder.us uses TIGER/Line street and highway information provided by the US Census Bureau from 2004, and as a result, not all the addresses were able to be geocoded. Approximately 10,000 of the 27,000 home sales were not able to be located on the 2004 TIGER/Line maps; these are homes that were built on streets that did not exist a decade ago. This introduces a significant potential for bias in the data, and a re-examination of this paper’s analysis with fully geocoded data may prove insightful. Summary statistics for the full and geocoded datasets are provided below.

Table 1: Summary statistics for the full and geocoded datasets


As expected, the geocoded dataset contains homes that are older, and as a result this reduced dataset contains homes with a lower average sale price, heated area, number of bedrooms, and number of bathrooms. The below graph and table show the data in both datasets distributed by sale year, both in terms of absolute sale numbers and as a percentage of the overall dataset.

Figure 1: Distribution of sales by year for the full and geocoded datasets


Table 2: Distribution of sales by year for the full and geocoded  datasets


Even though the geocoded dataset skews older and cheaper, the proportion of sales occurring in each calendar year is still roughly the same as the full dataset, indicating that the geocoded dataset is still very similar temporally to the full dataset. The effects of the subprime mortgage crisis on Durham home sales can be seen in both datasets; sales in 2008 and the years following are clearly depressed compared to 2005 through 2007. There is also evidence for a strong recovery in the past few years, and 2014 sales were at approximately the same level as the 2006 and 2007 peak.

We now run a hedonic regression on the logarithm of sale price for the geocoded data to examine the dataset. The regression used is below:

li_eq_1The sale price, heated area (in square feet), and bedrooms term are straightforward. The β3 is for the “bedroom/bathroom differential” which is the absolute value of the number of bedrooms minus the number of bathrooms for a house. This was motivated by a model developed by the National Association for Home Builders (NAHB) that aims to estimate home values. As Emrath (2006) describes, their data indicated that home buyers tend to prefer a rough balance between the number of bedrooms and the number of bathrooms. If a home has more bedrooms than bathrooms, an additional bathroom will increase the value of the home by a higher percentage than if the differential were smaller. The β4 is associated with a dummy variable for whether a property has a garage or not. The results of this regression are below.

Table 3: Regression on the geocoded dataset


The p-values for each of the variables is virtually zero. All else equal, an additional bedroom will add 15.3% to a home’s value and a garage will add 13.4%. The previously discussed bedroom/bathroom differential shows a fairly strong negative effect as expected. The NAHB model predicted that an additional full bath would add approximately 20 percent to a home’s value which is very consistent with the derived 19.8%. Heated area is measured in square feet and so an additional square foot has a very small positive effect as would be expected.

To make sure that there were no abnormal price trends present in the dataset, I established a price index using the data. This index is graphed below along with the Case-Shiller national home price index and the Case-Shiller home price index for Charlotte, NC, the nearest metropolitan area to Durham for which an index is provided. For each month, the Durham index is calculated as the average price per square foot of single-family residential home sales in the past three months; the Case-Shiller indices use a three-month moving average as well. The Case-Shiller indices use repeat sales of the same home to track changes, but the Durham market is not large enough to create an accurate index using this same method. As seen in the graph below, however, the price per square foot measure for Durham roughly approximates the Case-Shiller indices, suggesting that Durham home values have followed the same trends as other North Carolina cities (Charlotte) and the nation as a whole. It should be noted that it is merely a coincidence that the Durham index is similar in magnitude to the Case-Shiller indices.

Figure 2: Indices for home prices


Store Data             

This study analyzes the effects that the openings and closings of Walmart, Harris Teeter, TROSA Grocery, ALDI, and Save-a-lot grocery stores have had on nearby housing prices. The locations to be examined are listed below along with relevant dates. Other grocery stores can be found in Durham today, but the previously listed stores are the ones who have opened or closed stores in the past decade. These locations were manually geocoded using Bing Maps.

Table 4: Locations to be studied and relevant dates



The distance between each home and store was found using the Haversine formula which gives the great circle distance between two points on a sphere:


d = the distance between the two points

r = the radius of the Earth (3961 miles)

lat1, lat2, long1, long2 = the latitudes and longitudes of the two points

Borrowing from Pope and Pope (2014), the hedonic regression used was:


The logarithm of the sale price of a home can be explained through its property characteristics as previously defined and its geographic relation to a given store. Three indicator variables (D0.5, D1, D2) represent whether the given house is within 0.5 miles of a store, between 0.5 and 1 mile, or between 1 and 2 miles, and whether the home is between 2 and 4 miles away is the omitted indicator. Holmes (2011) in his research on Walmart considered a radius of two miles to properly constitute a store’s neighborhood, and this study will do the same for all the stores. The last group of homes 2 to 4 miles away will act as a control group for the homes inside the neighborhood that we are interested in examining.

The variable Post represents whether the sale occurred after the opening or closing of the given store. A sale occurring before the relevant date will have the relevant indicator (D0.5, D1, D2) set to 1, and the interaction between the Post term and the indicator variables inside the parentheses will lead to the corresponding indicator becoming zero. A sale after the relevant date will have both indicator variables at 1, and so the estimates for the second set of spatial estimators ( ) will provide information on whether the store impacted housing prices. This study uses home sales two years before and after each store’s opening or closing.

The difference-in-differences specification is used in order to remove the effects of omitted time-invariant variables that could bias estimates. Comparing homes sold before and after the store opening or closing allows one to more safely disregard neighborhood characteristics that would influence housing prices since these characteristics should more or less influence both the pre and post home sales in the same way. Holmes (2011) created and argued for a model in which Walmart stores are not placed based on characteristics of the nearby markets but rather on distribution and shipping costs. This would imply that the less than two and two to four mile ranges should be similar enough to properly control for broader housing market trends. For time-related variables, Figure 2 presented previously shows that the price per square foot for single-family residential homes in Durham has remained within a $20/sqft range for the past decade. While this is not an insignificant fluctuation, this index shows a reasonably flat trend as a whole so it is unlikely that including time-related variables would significantly influence the analysis.



The results for the first Walmart store located at 1010 Martin Luther Jr Pkwy showed a positive 14.4% effect on homes 0.5 to 1 mile away and positive 8.3% effect on homes 1 to 2 miles away after the store was opened. The Walmart store located at 1525 Glenn School Rd showed no significant effects at any distance, both before and after the store opened. This means that the Walmart was placed into a neighborhood which was similar price-wise to its surrounding area and that the opening of the Walmart did not influence nearby prices. It should be noted that this Walmart featured fewer home sales in its relevant subset of the dataset; its opening was in 2009 during the housing crisis when fewer homes were being sold, and it is located in the northeastern part of the city which is less dense. This Walmart had 1343 sales within four miles in its four year window compared to 2281 for the first Walmart store. This smaller dataset perhaps contributed to the lack of significant coefficients found in the regression.

The Walmart store at 3500 Roxboro Rd closed around the same time the previous Walmart opened. Before the closing, homes 0.5 to 1 mile away sold for 12.4% more than homes 2 to 4 miles away and homes 1 to 2 miles away sold for 23.0% more. The coefficients associated with the post-closing indicator variables were not statistically significant, so the closing of the store did not change nearby housing prices from the previous baselines. Only one of the three Walmart stores openings/closings in Durham caused nearby housing prices to change, indicating that the positive average effects found in Pope and Pope (2014) are not necessarily applicable to the stores local Durham area.

Harris Teeter

The three Harris Teeter store openings had significant results in terms of the neighborhoods they were placed in though none of the three showed any significant effects on nearby housing prices after the stores were opened. The Harris Teeter store at 2017 Hillsborough Rd was placed in a location where houses 1 to 2 miles away were worth 9.3% less than homes slightly farther away. The 1501 Horton Rd store was placed where homes 0.5 to 1 mile away were worth 10.2% less and homes 1 to 2 miles away were worth 9.2% less. The 1125 West NC 54 Hwy store was placed where homes 0.5 to 1 mile away were worth 11.8% more and homes 1 to 2 miles away were worth 8.5% more. While the coefficients are similar in magnitude, the differing signs suggest that there was no deliberate effort by Harris Teeter to locate its stores in certain kinds of neighborhoods. The regressions also show that none of the three stores had an impact on surrounding housing prices after they were opened. Homes near the first two Harris Teeter stores were still worth approximately 10% less and homes near the third store were still worth about 10% more.

TROSA Grocery

TROSA, Triangle Residential Options for Substance Abusers, Inc., opened a grocery store in East Durham, at 2104 Angier Ave, in 2010. East Durham is a significantly poorer area of the city, and the regression quantified this economic difference. Before the store opened, homes within 0.5 miles of the location were worth 74.7% less, homes 0.5 to 1 mile away were worth 70.0% less, and homes 1 to 2 miles away were worth 53.6% less as compared to homes slightly farther away. After the store opened, prices of homes within 0.5 miles fell 65.3% further, and homes 0.5 to 1 mile away fell a further 24.3%. Due to low traffic and high operating costs, the store closed in 2012. As expected from the previous regression, before the closing, the homes near the store were worth significantly less than homes farther away. After the store closed, home prices near the store actually increased; homes less than 0.5 miles away experienced a 35.9% increase from the previous baseline, and homes 1 to 2 miles away experienced a 33.3% increase.

These results do not necessarily mean that the grocery store exacerbated the poor economic conditions of the East Durham area. The store was located two miles from downtown Durham, an area that has seen significant investment and growth in recent years. Since the control group for the regression uses home sales two to four miles away, increasing prices in the downtown area would lead to homes within two miles becoming worth relatively less. While the grocery store was the first in fifty years to open in the neighborhood, offering nearby residents a source of fresh produce within walking distance, these results show that any positive effects that this increased food access provided were not significant when comparing East Durham’s poor economic conditions to downtown.


The ALDI store that opened at 7906 NC 751 South showed no significance for any of the distance indicator coefficients. This store is located near Southpoint Mall, and so it is unsurprising that the store opening did not change nearby housing prices given the very large number of shopping options already available in the area.


The regression for the Save-a-lot store opening was unique in that it showed significance for all of the distance coefficients. Before the store opening, homes within 0.5 miles were worth 75.6% less than farther homes, homes 0.5 to 1 mile away were worth 78.8% less, and homes 1 to 2 miles away were worth 8.4% less. Save-a-lot is located about a mile away from downtown Durham in the same East Durham area as the TROSA Grocery store was, and so these results make sense considering the much higher value of homes downtown compared to East Durham. After the store opened, homes within 0.5 miles experienced a 45.4% price increase, homes 0.5 to 1 mile away experienced a 33.2% increase, and homes 1 to 2 miles away experienced an 11.4% increase. It is unlikely that the opening of a single discount grocery store single-handedly led to these dramatic price increases, and so these results need to be interpreted with caution. We should note that the closing of the TROSA Grocery store led to similar increases; both the opening of the Save-a-lot and closing of the TROSA Grocery occurred in 2012, and we can hypothesize that East Durham experienced some sort of revitalization during this time that led to these results.


This paper shows varying results for the impacts of store openings and closings in Durham. While Pope and Pope (2014) found that new Walmart stores slightly increase nearby housing prices, this study did not find conclusive support for their conclusion. As a whole, the results for the different stores were difficult to interpret, and no retailer as a whole showed a definitively positive causal impact. This study shows that while there may be beneficial trends on a larger scale from introducing a new store into a neighborhood, the impacts of individual stores on home prices can be difficult to predict.


Emrath, P (2006). “How Much is a Bathroom Worth?” National Association of Home Builders. Retrieved from http://www.nahb.org/generic.aspx?genericContentID=62422.

Holmes, M. (2011). “The Diffusion of Wal-Mart and Economies of Density.” Econometrica 79: 253-302.

Pope, D. and J. Pope (2014). “When Walmart comes to town: Always low housing prices? Always?” Journal of Urban Economics 87: 1-13.

Historic Designation and its Effect on Durham Home Prices

by Bernadette Lowell  DP_LowellBernadette


Historic designation and the process of historic preservation have saved homes and commercial properties across the country from being torn down for newer construction. As space becomes sparse in larger cities, occasionally it is necessary to tear down these properties, however this can cause unpleasant construction and “mismatched” neighborhoods with homes from many different eras. Through national and local historic designation, owners can receive tax breaks and other incentives to keep their home in its original, historic form.

Durham is a prime example of a city that would need historic designation. Although it is not lacking for open land, there is a rich history in many downtown buildings and homes that needs to be preserved. Tobacco factories line downtown streets along with many homes dating back to the 1920s and 30s.

Durham has sought to preserve its local historic resources by “inventorying historically significant structures in the City and County, designating local historic districts and landmarks, establishing and supporting the Historic Preservation Commission (HPC), and nominating properties and districts for listing on the National Register of Historic Places.”(City of Durham) The HPC meets throughout the year to approve changes for any historic home and possibly designate new neighborhoods. Homes in any of the designated neighborhoods are taxed at 50% of the properties’ value. (City of Durham) However, with this local designation come certain restrictions. In order for any change to be made the exterior of the building, the owner must receive approval in the form of a Certificate of Appropriateness after a meeting with the HPC. (City of Durham)

A few homes and neighborhoods in Durham are also nationally designated. According to the National Register of Historic Places website, Durham county has 77 listed historic places and districts. [1]Although this can mean some federal tax breaks, there are little to no restrictions on any changes to the homes.

Historic neighborhoods in Durham

This study primarily looks at homes in two of the city’s seven historic districts. The Lakewood Park Historic District (Fig. 1), in southwest Durham, was listed as a national historic district in 2003. (Lakewood Form) It includes the blocks 2002-2112 Chapel Hill Road; 1601-1907 West Lakewood Avenue; 1406-1602 James Street; and 1809-1819 Bivins Street. (Lakewood Form) According to the application for historic designation, the buildings were built in 3 “generations” from 1902-1920, during the 1920’s, and in the mid 1930’s. These houses progressed from one-story homes with “modestly stylish Queen Anne features” to bungalows and into the “Minimal Traditional style.” (Lakewood Form) Many of these homes retained a high level of integrity throughout the years, making them prime candidates for historic designation and preservation. Each contributing home was constructed before 1952 and maintains enough of the original design and workmanship to be considered historic.

The Holloway Street District (Figure 2) was nationally designated in 1985 and is also considered to be a local historic district. The neighborhood, located much closer to the downtown area, dates back to the 1860’s, though it was reported in the application that many of the homes only date back to the 1880s through 1920’s.  At the time of designation, many homes were “intact but deteriorated,” with some left vacant and vandalized. (Holloway Form)


For the following regressions, I primarily used data from Zillow.com, which listed the number of bedrooms and bathrooms, square footage, lot size, year built, date and price of last sale, and their own “Zestimate.” This number is Zillow’s own estimate of the home’s market value.[2]

The Lakewood Park Historic District has 83 designated properties. Of these, 13 were commercial properties, vacant lots, or did not have enough information on Zillow, and 8 others were considered “multifamily” and were not included in the regression. These 62 homes in the district had an average Zestimate of $163,167 with a median of $154,112 and were on average built in 1931 with a median of 1924. (Figure 3)

The historic neighborhood was compared with the surrounding area, which is not historically designated on the national or local level. Using similar constraints—no multifamily or commercial properties— and removing any home without information on Zillow, 48 properties were chosen. These homes had an average Zestimate of $115,824 with a median of $108,780. On average they were built in 1950. (Figure 4)

The Holloway Historic District has 29 total homes. Seven of these had no information on Zillow and another three were considered multi-family. Of these 18 total homes, the average Zestimate was $165,739 with a median of $139,378. On average, the homes were built in 1931 with a median year of 1928. (Figure 5) The Holloway homes were compared with 84 surrounding homes. These homes had an average Zestimage of $69,912 with a median of $62043. They were, on average, built in 1934 with a median of 1925. (Figure 6)


For the following regressions, I used the formula lnZestimate=F(Historic, Characteristics) where Historic is a dummy variable for historic designation, and Characteristics include number of bedrooms and bathrooms, the square footage, lot size, and year. This hedonic model is in semi-log form, which implies that the coefficients for each explanatory variable are the percentage change in price with each one unit increase in that variable. From these variables, it is clear that there will be correlations between these explanatory variables, designation specifically being negatively correlated with year and positively correlated with other features of the house. Including all available Characteristics variables decreases the bias of the Historical variable.

For my initial regression, I only included Historic as an independent variable. In the Lakewood neighborhood, designation was found to have a positive influence on the Zestimate, with a coefficient of .317 and a t ratio of 5.52. (Figure 7) For homes in the Holloway district, designation had a positive influence on the Zestimate with a coefficient of .738 and a t ratio of 8.66. (Figure 8) Though both historic neighborhoods had a similar mean price, this initial regression reveals a significantly stronger influence of historic designation on the Holloway district. This could also be clearly seen through the mean Zestimates of the neighborhoods, as it is evident that the area surrounding the Holloway neighborhood contains considerably cheaper homes.

Following this regression, I included the rest of the independent variables for the characteristics of the home. In the Lakewood neighborhood, historic designation had a similarly high, statistically significant coefficient of .105 and a t ratio of 2.63. (Figure 9) This implies that, even after taking into account the structure of the home, historic designation brings a 10% increase in property value for the Lakewood neighborhood.

However, in the Holloway neighborhood, Designation had a coefficient of -.0019, though with a t ratio of -0.03. (Figure 10) Unfortunately, it is hard to draw any conclusions from this regression, as it is statistically not significant. Inclusion of a wider set of observations and explanatory variables could ultimately help this regression analysis.


From these regressions, I can conclude that the Lakewood historic designation significantly increases property value compared to the surrounding homes with the given data. Unfortunately, I cannot draw any conclusions after performing regressions on the Holloway District data.

If it is true that historic designation has lead to increased property values, then there are some potential policy issues that Durham will face. If these homes are in poorer areas, then an increase in property value could drive way poorer residents. If a neighborhood is locally designated, there is also an added burden to keep the home in its original form—requiring any change or fix to be approved by the HPC. In order to keep these homes affordable and less burdensome, there will need to be policy to keep residents in their historic home.

In order to conduct a better analysis of the impact of historic designation on home prices in Durham, this study would need more observations. As there are seven locally designated and fifteen nationally designated neighborhoods, there could be a large change in the outcomes with these homes included in the analysis.

This data set also did not include other variables about the features of the homes. Although they might not be significant, if a home has a garage, an attic, or basement could factor in to the property value as well as what the home is made of or even if it has been foreclosed on in the past. With these, the regressions might be more accurate.

One final inclusion to this data set could also be time. This regression does not show how the home prices have changed since the historic designation. A follow up study could look into whether or not these home prices have increased or decreased more rapidly than those homes in the surrounding blocks.



Figure 1



Figure 2















Map taken from: http://durhamnc.gov/ich/cb/ccpd/Documents/Historic%20Preservation%20Information/Historic_Resources_34x44_020312.pdf


Figure 3

Lakewood Historic Homes (N=62)

  Zestimate Bed Bath Sq. Ft. Lot Year built















Figure 4

Lakewood Non-historic Homes (N=48)

  Zestimate Bed Bath Sq. Ft. Lot Year built















Figure 5

Holloway Historic Homes (N=18)

  Zestimate Bed Bath Sq. Ft. Lot Year built















Figure 6

Holloway Non-historic Homes (N=84)

  Zestimate Bed Bath Sq.Ft. Lot Year built
















Figure 7

Lakewood Neighborhood, first regression


Figure 8

Holloway neighborhood, first regression


Figure 9

Lakewood Neighborhood, second regression




Figure 10

Holloway Neighborhood, second regression





“Historic Preservation.” City of Durham. N.p., n.d. Web. 29 Mar. 2013. http://durhamnc.gov/ich/cb/ccpd/Pages/HPC%20Items/Historic-Preservation.aspx

Individual Property Form for Holloway Street District. June 1984. Http://www.hpo.ncdcr.gov/nr/DH0188.pdf.

Leichenko, Robin, N. Edward Coulson, and David Listokin. “Historic Preservation and Residential Property Values: An Analysis of Texas Cities.” Urban Studies 38.11 (2001): 1973-987. Sage Journals. Web. 4 Feb. 2013.

USDI/NPS Registration Form-Lakewood Park Historic District. 07 Mar. 2003. Http://www.hpo.ncdcr.gov/nr/DH2541.pdf.






[1] http://nrhp.focus.nps.gov/natreghome.do

[2] http://www.zillow.com/wikipages/What-is-a-Zestimate/

[3] The following regressions are similar to those used in Historic Preservation and residential Property values: An Analysis of Texas Cities, which was presented in my literature review.

School Reports vs Housing Prices in Durham

By Bill Hoch

Literature Survey: The Effect of School Quality on Housing Prices

by Lauren Taylor Lauren_Taylor_Literature_Survey (1)

There are many factors that go into an individual’s or a family’s decision to purchase a home.  Such factors include structural characteristics of the house such as square footage and number of baths, property tax levels, proximity to amenities, neighborhood quality, and school quality, all of which are reflected in a house’s retail price.  Of particular interest to homeowners, economists, and policy makers is the effect of school quality on housing prices in any given area.  Numerous individuals have performed studies attempting to quantify how much individuals value school quality by analyzing housing prices in school districts of different quality schools.  Some studies have placed emphasis on “output-based” means of measurement such as standardized test scores and school ranking while others have used “input-based” measurements such as teacher-pupil ratio and per-pupil spending.  Most recently, researchers have focused on the use of standardized test scores as a measure of school quality and have compared these to the prices of housing in corresponding school districts.


Much of the research on the impact of school quality on housing prices can relate back to the model developed by Charles Tiebout in his 1956 paper, “A pure theory of local expenditures”.  In conducting his research, Tiebout’s model predicts that consumers pick a community to reside in based on which community best satisfies their preference patterns for local public goods (LPGs), which include schools, parks, and other amenities.  Furthermore, these individuals will move to the community whose local government best satisfies their sets of preferences, resulting in individuals self-sorting into homogenous communities with residents who demand equal levels of quality of LPGs (Tiebout, 1956).  This model can be applied to the analysis of school quality on housing prices.  According to such a model, individuals with similar preferences will populate a community.  Thus, individuals who prefer better quality schools may be willing to move to a community in which they pay higher housing prices because their desired LPG quality exists there.


Many studies have been conducted in the past two decades to further explore the effect of school quality on housing prices in various cities across the United States.  The Reinvestment Fund (TRF) conducted such a study which measured school quality in Philadelphia and quantified its impact on the value of Philadelphia real estate.  In this study, residential sales between 2006 and 2007 are geocoded and combined with data on the elementary zone  in which each sale lay as well as the percent of elementary school students scoring proficient or above on the combined Pennsylvania System of School Assessment (PSSA) for Reading and Math at the schools in that zone.  TRF also used a multilevel modeling analysis that accounts for correlation at various levels and among several factors, in its study in order to more accurately assess the relationship between school quality and housing prices.  This model takes into account the fact that other neighborhood characteristics are correlated with school quality and may affect the prices of housing in a given school district as well.  This model attempts to get rid of some bias, but some bias still remains due to omitted variables that haven’t been controlled for.   This study concluded that point increases in Structural Decline scores, which measure the impact of new construction and neighborhood disinvestment on housing prices, reduce sale prices of homes by $1.50 per square foot.  Similarly, point increases in Crime scores, which measure the impact of crime on housing prices, reduce the sale prices of homes by $1.00 per square foot.  Of even greater interest to this paper, TRF’s study found that for each percentage point increase in school district PSSA score of students who scored proficient or above, the prices of housing in that area increase by $0.52 per square foot. The correlation between housing prices and school quality can be seen in the Figure 1 below.  The two maps in Figure 1 show that central Philadelphia districts tend to be of lower housing price and lower school quality, and higher quality school districts and districts with higher housing prices tend to be clustered in the top right and top left regions of the Philadelphia area.  Thus, this study shows that overall school quality, as measured by test scores, is positively related to the price of housing in that school district.


Figure 1: Median sales price by school catchment (Map 1) and Percent of elementary students scoring proficient or above on PSSA (Map 2)


In another study, Kwame Owusu-Edusei and Molley Espey used data on housing transactions between the years of 1994 and 2000 to estimate the effect of K-12 school rankings on housing prices in Greenville, South Carolina.  These two researchers used a relative measure of school quality – school rankings – rather than an absolute measure.  Like many of the other studies that will be discussed later in this paper, this study applied a hedonic pricing model to estimate the quality of schools on housing prices.  Owusu-Edusei and Espey made two important conclusions from their data: 1) high-ranked schools have values embedded in single-family housing prices and 2) greater commuting distances to schools has a negative impact on the value of property.  In this specific hedonic housing pricing technique, the price of a house in Greenville, SC was modeled as a function of the following characteristics of a house: structural characteristics including condition, number of baths, square footage, air conditioning, lot size, and garage; block characteristics; proximity to parks, golf course and schools; and school rank categories.  The study then used ordinary least square estimations of a semi-log model and regressions to interpret the data collected.  As will be seen in many other studies documented, Owusu-Edusei and Espey found that proximity to and quality of a school does affect the prices of housing in its respective school district.  Of the houses studied in Greenville, SC, houses with elementary schools within 2640 feet (a half of a mile) of their properties have prices 18% higher than those of houses located further than 10560 feet (2 miles) from an elementary school.  Similarly, houses with middle schools within 10560 feet of their properties have prices 16% higher than those of houses located further than 10560 feet from a middle school and houses with high schools within 10560 feet of their properties have prices 12% higher than those of houses located further than 10560 feet from a high school.  Furthermore, if an elementary school rated Good, houses in that school district sell for 12% higher than those in districts with schools with a worse rating.  If a middle school rated Average, houses sell for 31% higher than houses in a district with a school of a worse rating.  Lastly, if all K-12 schools in an area rated Average and Above, the value of homes is 19% higher in that area than those in areas with Below Average schools.  Thus, it can be concluded that both greater proximity to and better quality of schools does positively affect the prices of housing located in their attendance zone.


Sandra Black conducted a study of great importance, which also used housing prices – this time in suburbs of Boston, Massachusetts – to infer the value homeowners place on school quality (1999).  Black used a sample of single-family residences within 39 school districts across 3 counties outside of Boston from 1993 to 1995 and used test scores on a statewide 4th grade assessment called the Massachusetts Educational Assessment Program as her measurement of school quality.  Black set out to calculate how much more people are willing to pay for houses located in areas with better schools.  However, Black made an extremely important observation that such a calculation can be complicated by the fact that better schools tend to be located in better neighborhoods, a characteristic which also influences the price of housing.  Black concluded that estimates of the effect of school quality on housing prices that do not adequately control for neighborhood characteristics may overestimate the value of better schools.  Thus in order to control for variation in neighborhood characteristics, property taxes, and school spending, Black used a hedonic housing price regression – which again describes house sale price as a function of the characteristics of the house and its location – that includes boundary fixed effects which restrict the sample of houses studied to those close to and on opposite sides of school attendance district boundaries.  Instead of the traditional hedonic price function, Black used the formula ln(priceiab) = α + X’iabβ + K’bφ + γtesta + εiab in which boundary dummies (the K term) account for unobserved characteristics shared by houses on either side of the attendance district boundary.  In this way, Black was able to eliminate bias caused by omitted variables such as neighborhood characteristics and property taxes.


Black conducted her calculations twice: once using a simple hedonic housing price regression and once using a hedonic housing price regression which incorporated boundary dummies.  Results from the simple hedonic regression indicate that a 5% increase in the average elementary school test score is associated with a 4.9% increase in house prices in that school district, as shown in Table 1 below.  However, results from the hedonic regression which controlled for omitted variable bias such as neighborhood characteristics show that when houses observed are restricted to those within only 0.15 miles from the boundary of a school attendance zone, a 5% increase in the average elementary school test score is associated with a 2.1% increase in house prices in that school district (Table 1).  This second calculation is roughly half of the estimated effect calculated using the simple hedonic housing price regression.  Thus, in her paper, Black has demonstrated that it is very important to control for neighborhood characteristics by restricting the housing sample used to houses on school attendance boundaries.  Otherwise, one may greatly overestimate the value of school quality as shown by test scores on the prices of housing in the respective district.  Yet, even after controlling for omitted variables, it can be seen that better school quality, as shown by an increase in test scores, has a positive effect on housing prices.

Table 1: Magnitude of Results



Using both the work of Tiebout and Black as background research, John Wulsin more recently conducted a study on the effects of school quality on housing prices in Durham, North Carolina.  In his paper “An Analysis of the Effects of Public School Quality on House Prices in Durham, North Carolina” (2009), Wulsin stated that when families buy a home, they also buy the right for their kids to attend the local public school in that district and that the price of that right is incorporated into the price of the house they purchase.  Like Black, Wulsin used school performance composite test scores, which is the North Carolina Department of Education’s standardized metric for measuring school quality.  Building on Black’s work, Wulsin, too, recognized the importance of using boundary fixed effects to control for neighborhood characteristics in order to ensure that the effects of school quality on housing prices are not overstated.  Wulsin gathered the following data on houses in Durham County: the fair market value of the house, observable characteristics that affect house prices, which school attendance zone the house is in, and the distance the house is to the border of the school attendance zone.  He gathered data from sources such as the Durham Tax Assessors Office and Durham Public Schools.  Wulsin then worked with GIS shapefiles, computer software programs such as ArcMap, StatTransfer, and Stata, and an OLS regression to organize and decipher the data.  After collecting and interpreting his data, Wulsin concluded that “parents do pay more to live in areas with better schools”.  Wulsin’s study found that in Durham Country school districts, a 10% increase in elementary school scores leads to an 11% increase in housing prices, a 10% increase in middle school scores leads to an 11% increase in housing prices, and a 10% increase in high school scores leads to a 5% increase in housing prices.  Thus, it can be seen that an increase in school quality, as measured by test scores, once again leads to an increase in housing prices in that school attendance zone.


As with many other attractive neighborhood qualities, it has been observed that people value the quality of the schools they send their children to.  In fact, a 2000 survey by the Philadelphia City Planning Commission found that the third most important neighborhood characteristic for buyers and sellers with children was the presence of a good school in the area (TRF, 2007).  Many researchers have attempted to determine the exact value consumers place on school quality and have used housing prices as a means of measurement.  As shown by the studies analyzed above, better school quality is correlated to higher housing prices.  Furthermore, this trend has been observed across the United States.  However, there are still several issues that need to be explored further.  Firstly, measuring school quality is often difficult and very subjective.  Many have argued for the use of input-based metrics such as per-pupil spending while others believe that output-based measurements such as test scores are a better indication of school quality.  Future studies should attempt to collect and use a greater range of data on each school observed in order to gain a clearer picture of what makes a school “good quality”.  Furthermore, researchers need to continue to develop better ways to isolate the effects of school quality on housing prices and reduce, with an ultimate goal to eliminate omitted variable bias in which variables such as neighborhood characteristics and property taxes cause an overestimate of the impact of school quality on housing prices.  In conclusion, as each of these papers has shown, the quality of a school has a positive impact on the prices of houses located within that school attendance zone.  This finding is not only important to homeowners and parents, but also to economists and policy makers.  Because it has been found that better quality schools increase the real estate value of houses in their areas, improving schools can be a method for improving neighborhoods and stimulating economic growth.





Works Cited:


Black, Sandra. 1999. “Do Better Schools Matter? Parental Variation of Elementary Education”. Quarterly Journal of Economics, Vol. 114. 4 February 2013.


Owusu-Edusei, Kwame and Molley Espey.  2003. “School Quality and Property Values in Greenville, South Carolina.” Department of Agricultural and Applied Economics, Clemson University. 30 January 2013.


The Reinvestment Fund.  2007. “Schools in the Neighborhood: Are Housing Prices Affected by School Quality?”  Reinvestment Brief: Issue 6. 30 January 2013.


Tiebout, Charles. 1956. “A Pure Theory of Local Expenditures”. Journal of Political Economy. 5 February 2013.


Wulsin, John.  2009. “An Analysis of the Effects of Public School Quality on House Prices in Durham, North Carolina.” Economics Department, University of North Carolina at Chapel Hill.  2 February 2013.