Home » Posts tagged 'Property Values'
Tag Archives: Property Values
Green Space and Property Values
Although many of the benefits associated with public green spaces are seemingly obvious and easy to describe, they are often much harder to quantify. Green spaces in urban and suburban areas have typically been publicly provided amenities that have no set market price, but it has become increasingly common to evaluate them in terms of their monetary contributions to their surrounding communities. There exists a need, therefore, to convert the many assumptions regarding the inherent benefits of green space into objective, quantitative estimates of their worth (Nicholls 2005). Recent trends towards increased land development, particularly in urban areas, makes the ability to determine the economic values of public parks and green spaces important in order to ensure their existence and designation. Early literature supports the notion that green space causes an increase in property values because home-owners and renters are willing to pay more for the perceived benefits of being close to green space (Crompton 2001). However, more recent studies have been able to use the hedonic pricing analysis as a more accurate means of demonstrating the variable effects. Because green space offers many different benefits, such as environmental, recreational, transportation, aesthetic and health-related nature, no one method exists to measure all such benefits simultaneously (Nicholls 2005). On a similar note, not all green spaces are the same or provide the same amenities, and thus their impact on property value may vary. Therefore, this literature survey will discuss one essay that provides a foundation establishing the importance of a quantifiable measure for green space benefits on property value, and two studies that use regression analysis to measure the different variables that impact the perceived benefits of green spaces and subsequent property values.
Some city planners, urban developers, and governmental officials believe that development brings prosperity through enhanced tax revenues, and hence any land left open or undeveloped is considered a wasted asset. Furthermore, opponents of green spaces have identified several negative externalities, such as the invasion of the privacy of those residents whose properties directly adjoin greenways, concern regarding the numbers of strangers who will be passing through local neighborhoods, and fears of increased noise, littering, trespass, and vandalism (Nicholls 2005). All of these factors can (and in some studies have) decrease the generally believed positive impact that green spaces have on home values (Nicholls 2005). Crompton (2001) combats this perception through the establishment of the proximate principle. This principle suggests that the value of a specified amenity, like green space, is at least partially captured in the price of residential properties “proximate” to it (2001). If it is anticipated that properties or homes located near an open green space are considered desirable, the additional money that homebuyers and renters are willing to pay for this location represents a “capitalization” of the land into proximate property values (2001). With an increase in property value comes and increase in property taxes, and in some cases the additional taxes paid for all proximate properties may cover or even exceed the annual cost of acquiring, developing, and even maintaining the green space (2001). As such, many public parks were originally created with the hopes of their direct and indirect economic contributions to city tax revenues, Central Park in New York City being a prime example (2001). As a result, the impact that green spaces can have on the economic development of an area makes them an important factor of consideration in urban and suburban planning. Twenty out of thirty previous studies that Crompton (2001) discusses support the proximate principal; however, several other factors can influence the relationship between green space and property values, such as the various forms of desirability associated with green space and the physical characteristics of the green space itself.
Nicholls (2005) uses hedonic pricing to operationalize and measure Crompton’s proximate principle in a specific location and takes into account two different desirability interpretations of green space: aesthetic appeal and physical proximity. The greenbelt chosen for the study is the Barton Creek Greenbelt and Wilderness Park in Austin, Texas, along with three major residential bordering neighborhoods: Barton, Lost Creek, and Travis. The greenbelt is a 1,771-acre natural area located to the west of downtown, and includes 7.5 miles of multi-use trails, as well as various parking and restroom facilities (2005). Each neighborhood is examined separately since each contains a different set of locational amenities for inclusion in the hedonic model, but since properties were located within the same geographic sub-areas (such as school and tax zones) neighborhood and community variations were not investigated (2005). Sales price is the dependent variable, and the independent variables include three groups of property value influence: structural, locational, and environmental (2005). The value of the greenbelt is measured in three ways: aesthetic value, which is shown using two variables, direct adjacency to the greenbelt and view of the greenbelt; and physical proximity, which is represented by a continuous measurement of the distance between each property and the closest entrance to the greenbelt (2005).
The results of Nicholls’ hedonic analysis show that adjacency to the greenbelt produced significant property value premiums in two of three neighborhoods (Barton and Travis), but in no case did visual or physical access to a greenway have a significant negative impact on surrounding property prices (2005). The lack of positive impact of greenbelt adjacency in the Lost Creek area may be a result of the dramatic topography and dense vegetation that dominates the area. Lost Creek homes directly adjacent to the greenbelt are typically located on the edges of deep, thickly vegetated ravines that lack recreational access or nice views (2005). Conversely, homes located farther away from the greenbelt boundary on a higher elevation level have widespread views of both Austin and the greenbelt, but this view often includes a high voltage power line (2005). Although proximity to a power line is usually seen to have negative or neutral impact on property values, in this case the result could be that the beauty of the green space in the majority of the view offsets the interference of the power line into a part of it (2005). The finding of significant positive impacts of greenbelt adjacency in the other two neighborhoods supports this argument that physical characteristics may be influential (2005). In both the Barton and Travis areas, the topography is less steep and the vegetation is less dense, which might provide more obvious visual benefits (2005).
While the Lost Creek area did demonstrate the expected relationship of a decline in property value with increased distance from the closest greenbelt entrance ($3.97 decrease with each foot from the nearest entrance), in Barton and Travis the coefficient on the distance variable appeared insignificant (2005). An explanation for the Travis area isn’t clear, but for Barton this could be a result of the neighborhood’s distance to the bridge to downtown Austin. Being the closest of the three neighborhoods to downtown, it is possible that Barton homeowners tend to be work downtown and enjoy walking or biking to work, making the distance to downtown an important element (2005). Moreover, the Barton neighborhood enjoys easy access to many green spaces besides the Barton Creek Greenbelt and Wilderness Park, weakening the value of proximity to this specific amenity (2005). The city of Austin is known for its many open space amenities and downtown with several outdoor recreational opportunities (2005). While this analysis does emphasize the influences that variables such as topography, vegetation, and use patterns may have on the value of a green space amenity to local residents, there are other important variables that have not been accounted for, such as the type of green space.
A study conducted by Anderson and West (2006) uses home transaction data from the Minneapolis–St. Paul metropolitan area to analyze the relationship between the proximity to several different types of green spaces and property values. As suggested by Crompton (2001), the type and purpose of green space is an important factor to take into consideration. Anderson and West (2006) analyze several types of green spaces, including neighborhood parks, special parks, golf courses, and cemeteries. Special parks are defined as national, state, and regional parks, arboretums, nature centers, natural areas, and wildlife refuges, in order to differentiate them from neighborhood parks, which are generally more urbanized and provide fewer recreational opportunities and natural amenities (2006). Furthermore, their hedonic analysis differs significantly from Nicholls (2005) in that it allows the effects of proximity to depend a completely different set of variables, including population density, income, crime, age of the population, and distance to the central business district. In addition, they control for neighborhood characteristics and potential omitted spatial variables using local fixed effects.
The most significant from the analysis were in relation to population density, distance to CBD, income, and crime rates. The effect of green space on sales price depends on a home’s location and neighborhood characteristics. On a broader scale, Anderson and West (2006) find that urban residents in more densely populated neighborhoods located near the CBD place a higher value on the proximity to green space than suburban residents located further away from the CBD and in less densely populated areas: in neighborhoods that are twice as dense on average, the amenity value of proximity to neighborhood parks is nearly three times higher than average, while the amenity value of special parks is two-thirds higher (2006). This finding suggests that estimates of green space benefits for the average home in a metropolitan area will over/under-estimate the values of properties in particular neighborhoods. Consequently, conclusions from studies analyzing city preferences should not be used to draw implications for suburban planning. Additional results from the Anderson and West (2006) analysis highlight the effect of income on green space and home values. In neighborhoods that are twice as wealthy on average, the amenity value of neighborhood parks is more than four times higher than average, while the amenity value of special parks is more than two times higher (2006). Crime rates also proved to be a significant factor impacting green space values, in fact the amenity value of proximity to neighborhood and special parks rises with crime rates, so it appears that both types of parks act as buffers against the negative effects of crime (2006). Although conclusions based on the other previously mentioned variables were also realized from this study, they were not as significant as the four discussed above.
While the findings of Nicholls (2005) and Anderson and West (2006) focus on distinctly different green space areas (one being more urban than the other), they both provide quantitative measures to unravel the many factors impacting the proximate principle established by Crompton (2001). As the decentralization of cities continues throughout the 21st century and cities keep growing at their peripheries, the tradeoff between developing and preserving green space becomes an increasingly important debate. Although development can help fulfill a population’s needs for additional housing and commercial space as well as increase tax base revenue, green spaces provide a number of benefits, many of which have been discussed throughout this survey. Understanding the impact that green space has on property value will not only help regional developers and government officials make better decisions regarding the provision, design, zoning, and use of these public goods, but also help the creation and development of better homes and more desirable communities.
Anderson, Soren T. and West, Sarah E. “Open space, residential property values, and spatial context.” Regional Science and Urban Economics 36 (2006): 773–789. Web. http://www.macalester.edu/~wests/AndersonWestRSUE.pdf
Crompton, John L. “The Impact of Parks on Property Values: A Review of Empirical Evidence.” Journal of Leisure Research 33.1 (2001): 1-31. Web. http://www.actrees.org/files/Research/parks_on_property_values.pdf
Nicholls, Sarah. “The Impact of Greenways on Property Values: Evidence from Austin, Texas.” Journal of Leisure Research 37.3 (2005): 321-341. Web.
Assessing the Economic Impact of Sports Facilities on Property Values: A Spatial Hedonic Approach By: Xia Feng and Brad R. Humphreys
I. Research Question
Brad Humphreys, Dennis Coates and many other urban economists have conducted research in the field of sports arenas and urban development. However, most research has focused on identifying and analyzing tangible, economic benefits of sports arenas on cities. Differentiating itself from prior research on the intangible benefits of sports arenas on cities, Xia Feng and Brad Humphreys’ paper proposes a spatial hedonic model that estimates the intangible benefits of two sports facilities in Columbus, Ohio on residential property values.
This discussion of the benefits of sports stadiums stems from the willingness of cities and towns to subsidize construction of expensive sports stadiums. As the rise in the size of these subsidies has coincided with the boom in the construction of new stadiums, urban economists conducted research on the costs and benefits of construction of new stadiums and arenas. Proponents of these subsidies posit income increases, job creation and multiplier effects (due to new spending) as tangible, positive impacts of building new sports stadiums. However, contrary to the aforementioned claims, made mostly by consulting firms (usually hired by the respective sports franchises), the findings from years of economic research have shown no positive impact of building new stadiums on cities. In fact, econometric evidence has shown that professional sports facilities can have little effect to net negative effects on the local economy.
Regardless of these well-respected and well-supported research projects, cities continue to subsidize the construction of sports stadiums. The continuation of this policy decision, which research finds in general to be neither cost-effective for cities nor beneficial to cities, forces consideration of intangible benefits. Few papers have empirically estimated the intangible benefits, such as the increased civic pride, increased city attractiveness or increased cultural benefits, of building sports stadiums. A couple papers have examined the impact of sports facilities on property values with varying results, and this study adds to the literature by providing new evidence based on data from different locations and different sports. Most importantly, this study does not ignore spatial effects. Spatial autocorrelation is the correlation among values of a single variable due to their close locational positions on a two-dimensional (2-D) surface. Spatial autocorrelation could have caused biased estimates and model misspecification in the few earlier models on the subject of stadium presence’s impact on housing prices
II. Theoretical Background
Because of the difficulty of measuring “intangible benefits or costs”, Feng and Humphreys assume that the presence of a stadium would be viewed as an intangible characteristic and the presence of a sports stadium would be capitalized in housing prices. Housing prices tend to be spatially correlated due to common neighborhood characteristics.
Feng and Humphreys use an adaptation of the spatial lag hedonic model:
(I − ρW y) −1 = I + ρW + ρ 2W2 + . . .
This model links each observation of the dependent variable to all observations of the explanatory variables through a spatial multiplier.
Using transactions data, containing observations on 9,504 single-family housing units, for the year 2000, Feng and Humphreys analyze the values of residential housing around Nationwide Arena and Crew Stadium in Columbus. The data set includes housing and neighborhood characteristics such as lot size, school quality, environmental quality and number of fireplaces.
To account for aspects of the model that were not incorporated into the adapted spatial lag hedonic model, certain modifications were made to the model. To account for the presence of Ohio Stadium, dummy variables were created. To control for the effects of businesses on housing values, Feng and Humphreys controlled for the number of commercial establishments in each zip code, which allowed the business-related variables to capture some of the effects of business location on residential property values.
III. Empirical Model
Known as a spatial weighting matrix, this symmetric matrix is used to define the locations for which the values of the random variables are correlated, and the rows in the weights matrix are standardized. The features of both housing markets and individual housing data make the definition of the spatial weights matrix W especially important. The aforementioned matrices specify “neighborhood sets”, and these neighborhood sets capture spatial interaction. Feng and Humphreys use GeoDa to specify the neighborhoods and to define the spatial weights matrix, and begin by using four different spatial weights to create the matrices. Next, Feng and Humphreys use the log-log form of the hedonic housing price with the appropriate spatial lags to best estimate the parameters.
IV. Results and Discussion
The results of the research of Feng and Humphreys suggest that the presence of sports facilities in Columbus have a significant positive distance-decaying effect on surrounding house values. For Nationwide Arena, at the average, all else equal, for each 1% decrease in the distance to the arena is associated with a 0.175% increase in the price of the average house. In dollar terms, a 1% decrease in distance from each house to the arena, on average, increases the price of an average house by $222. The primary variable used to evaluate the effects of sports facilities on surrounding housing values is the distance between each house and the sports facility, and analysis of this parameter shows that the presence of sports facilities has positive effects (though they diminish with distance) on housing values. Importantly, Feng and Humphreys also show that prior OLS models, which did not account for spatial autocorrelation, overestimated the distance parameters, and did not correct for heteroskedasticity when present.
This paper elevated the credibility of the larger economic argument by finding the general importance of factoring spatial autocorrelation into property value modeling. With regard to policy decisions, professional sports facilities generate intangible benefits in the local economy, and cities do have a rational economic argument to lodge in support of provision of subsidies to sports stadiums. While the costs of public support rarely exceed the cost of public funding for the stadiums directly, the subsequent rise in property values can set the foundation for more substantial growth in adjacent areas, and give the city’s business community the confidence necessary to invest. Feng and Humphreys offered a more precise method of analyzing costs and benefits, and show that there are positive effects (contrary to most research) of building sports facilities at least in this one example. This paper offers answers, and poses new questions. What other benefits can be discovered? How close can economists make it to quantifying the efficient subsidy level for stadiums and arenas?
 Humphreys, Brad & Feng, Xia. “Assessing the Economic Impact of Sports Facilities on Property Values: A Spatial Hedonic Approach.” LASE/NAASE Working Paper Series 8.12 (2008): 1-20. Web. 25 March 2015.
By Valtcheva Katerina DP_ValtchevaKaterina
The part of Old North Durham around Foster St. and Geer St. has historically been an industrial neighborhood. It was generally considered a bad area of Durham despite its proximity to the central business district. In the past few years, this part of the city has been subject to some changes in development patterns, which have begun to transform the neighborhood. I will present my analysis of property-value movement in the area and examine it in the context of the new businesses that have been opening in the area. My hypothesis is that this recent commercial upturn, which was organic in origin, has had a positive impact on residential property prices in the part of Old North Durham that lies on and surrounds W Geer St. In order to confirm this hypothesis and quantify the impact of the new developments on existing structures, I have compared forty houses in Old North Durham (in proximity to Motorco Music Hall) with forty houses in East Durham and Edgemont over the last eight years. I have used Zillow estimates to obtain house value appraisals, collected annually, for the first month of each year starting in January 2006 and ending in January 2013.
Old North Durham initially developed with the construction of tobacco warehouses, as well as related commercial and industrial ventures nearby. North Durham Elementary School has had an influence on the area with respect to social development, while the Farmers market has also been influential in the area’s development as a center of Durham’s alternative lifestyle scene. However, for quite some time the area has been known as a rough part of town, and public perception to this effect has only recently begun to change. I believe that this trend started with the opening of several bars and restaurants in the area: Fullsteam Brewery was opened on August 13th 2010 on Rigsbee Ave; in 2011 a popular bar and music venue, Motorco Music Hall, was opened right across the street from Fullsteam; and a restaurant called Geer Street Garden, on the corned of Foster St. and Geer St., followed on May 5th the same year. Further, in the end of January 2013 a café called CocoaCinnamon—the area’s most recent addition—opened its doors across the street from Geer Street Garden. Interviews with the owners of these businesses revealed that a big part of what drew them to the area was not government policy, but a combination of its proximity to the city center and its industrial aesthetic: indeed, Fullsteam Brewery occupies a prior soda bottling facility, Motorco a car dealership, and CocoaCinnamon an automotive repair shop. The buildings that were chosen for these additions to Durham were not only located a short walk away from W Main St and the commercial heart of the city, but also happened to be particularly suitable for the type of bars, restaurants, and clubs that their owners were searching for. ,, 
The area I have chosen to compare Old North Durham to is similarly industrial, with its historical roots in milling and textiles. East Durham and Edgemont’s two textile mills have been turned into a retirement home and office spaces, which made them unavailable at the time when Old North Durham was chosen to host Motorco Brewery. Other reasons why I have chosen East Durham/Edgemont for my control area are that its houses are very similar to the ones around Motorco, built around the same time period, and that it is approximately the same distance away from the central business district as Old North Durham is. As of 2006, the area was more economically depressed than Old North Durham, but there are two reasons why I don’t believe that makes the results of this study less significant. The first is that, for the purpose of this study, I am interested in relative changes of values between the two neighborhoods pre- and post-2010. The second is that the difference would only be marginal to someone who is already taking the risk of opening a new business in Old North Durham. While the worse-off state the latter neighborhood does not distort the findings of the study, is nonetheless one of the factors that may have contributed to Geer St. and Rigsbee Ave’s housing Durham’s urban revival. However, this result is better explained by the fact that the industrial buildings existing in East Durham/Edgemont were either unavailable or poorly suited for housing breweries and cafes at the time Fullsteam opened.
Now I would like to focus on how I selected properties for use in a quantitative comparison of development in these two areas. House values representative of the Motorco area in Old North Durham come from forty homes that are located on one of the following streets: W Geer St., North St., Hargrove St., Glendale Ave, Northwood Cir, and N Mangum St. The average house size of this sample is 1375 ft2 (± 523 ft2.) The average lot size is 6121 ft2 (±1640 ft2.) The year these properties were built ranges between 1910 and 2004. The average year built is 1937 (±20) years. For my control group in East Durham and Edgemont I have chosen houses on one of the following streets: Hart St., S Driver St., Roberson St., Angier Ave, Vale St., S Plum St., E Main St., Clay St., and Ashe St. The average home size in the control area is 1432 ft2 (±476 ft2.) The average lot size is 7047 ft2 (±1413 ft2.) The year the homes were built ranges from 1900 to 1992 and averages to 1929 (± 21) years. Although the area I have chosen for the controlled sample closely resembles North Durham in many aspects, the house lots there were generally bigger than the ones in North Durham. For the purpose of creating a better group of comparable properties, I have excluded some houses with significantly larger lots. Moreover, the majority of houses in East Durham and Edgemont were built between 1900 and 1910. Therefore, I included in my sample as many of the later-built houses in the controlled area as possible in order to create a sample that more closely resembles the properties I had chosen in Old North Durham. With this changes, the group I control for has an average lot size of about 900 sq. ft. more than the sample of the North Durham area. The statistical analyses discussed below demonstrate that this is not a big enough difference to significantly distort the finding of my study, as the relationship between house prices and house size and amenities is much stronger than the relationship with lot size. This is especially noticeable in a town such as Durham where land is not a scarce commodity, as opposed to a place such as New York City, where such a difference in lot sizes would have been significant.
Figure 1: Property value information collected from zillow.com
To explore the trends within home values in Old North Durham and East Durham/Edgemont, I first normalized property values of each house by the square footage of the house. I also normalized each property value by the historic average price of housing in Durham at large for January of the corresponding year (fig. 1) in order to ignore the effects of the housing bubble and bust. For the time being, I have ignored the effects of lot size, as normalizing by that value would imply a linear relationship between lot size and overall property value—a false assumption in this environment. I first plotted mean property values in the two areas prior to 2010, in order to see how house prices in the two areas have been moving prior Old North Durham’s recent commercial development. I found the best-fitting line between the points for each group and I observed a positive change in house prices for this time period with a difference in their slopes of .0113 in favor of the control area (fig. 2 and 3). This does not indicate a significant difference in the house price movements between the two areas prior to 2010.
When plotting the data for all eight years, the picture changes dramatically (fig. 4 and 5). House prices continue to rise in Old North Durham through the entire time period (though, with the addition of datapoints through 2013, the slope of the price curve falls from .0206 to .0108), while the overall trend in house values in East Durham/Edgemont has become negative. This means that the recent fall of house values in the area has been so great that the slope of the line describing the overall trend of house prices went from positive .0319 (between 2006-2010) to negative .0275 (when datapoints are included through 2013); a total change five times bigger difference between the two areas prior to 2010. Running a Student’s t test comparing mean home prices on a year-by-year basis, it was observed that the difference in mean home values between the two neighborhoods was not statistically significant until 2010, from which point it became consistently significant (p<<0.001) (fig. 6). These findings suggest that the rise of new businesses in Old North Durham have correlated with a buoying of property values in the surrounding area, while a similar area in Durham has experienced a statistically significant fall in residential property values.
In order to get a fuller idea of how the relationship between house prices in the area have changed, I ran a total of sixteen linear regressions of house prices versus home size, lot size, and home age in each area over the years investigated. These regressions gave me a measure of how much house price depends on these parameters. My findings are summarized in Figure 7. From the first graph we observe a substantial marginal change in home value in Old North Durham with square footage. While the result in 2009 appears to be an outlier, all other years show a difference between the price dependence on square footage between the two areas that has been stable up to 2009 and then diverging since then (with only a small change in Jan 2013). The divergence we see since 2009 shows that the new developments in Old North Durham have increased the gap between the willingness of people to pay more for extra square footage in between these two neighborhoods. The graph also illustrated that this divergence is not due to a significant increase in the customers’ willingness to pay for additional sq. ft. of a house around the Motorco Area as much as a decline in their willingness to pay for the same sq. footage in the control area. This is consistent with what we saw in Figures 4 and 5.
The second graph on figure 7 shows the marginal change of house prices with lot square footage. This does not represent a strong or clear relationship in these areas, either within each neighborhood over time, nor between them. One way to interpret this is the possibility that residents’ decision to buy a house in a poorer and worse neighborhood may not be influenced by the lot size as much as the ones in nicer neighborhoods. However, the small correlation between lot size and home value supports the idea that lot size does not really influence a residential property’s price in a city like Durham where land is abundant.
When looking at the third graph on figure 7, one must note that this the regression is performed on year built rather than on age, which means that the graph shows the relative change in home value for each additional year you add to a home’s age. Interpreting the relationship we see that on average older houses in the Motorco, or Old North Durham, area cost more than older houses in the control group. This is an interesting observation that could mean that there are more renovations in Old North Durham, while more renovations mean that the area has been taking a turn for the better since the new businesses came in.
There is more evidence that Old North Durham has begun to transform in the recent years. From my interview with Fullsteam’s owner I learned that he has noticed his clientele becoming more diverse though the years, and that a lot more students are comfortable stopping by than before. This is evidence that Old North Durham has improved in terms of safety and could be attributed to the opening of the new businesses—not only the new bars and restaurants, but also the food trucks, which are now stopping by regularly, make the area around Old North Durham a lot more vibrant than it used to be. The increased attention to this part of town is also evident from a dispute over a nearby area that is currently used as a soccer field. The disagreement comes from the fact there seems to be a conflict of interests between the members of the community over the use of the land after a proposed renovation. Some residents are using this area to practice soccer and claim Durham does not have enough soccer fields based on national standards. However, others believe that the area should host a smaller soccer field and some playgrounds. “The park has been included in two Capital Improvements Plans, first in 2001 and again in 2005, but the city has not funded any upgrades there, saying money was better spent in other parks.” The increased attention towards the soccer field debate today supports the idea that the area has began to take a turn for the better and could even suggest that gentrification is occurring.
The outcomes of this analysis provide strong evidence towards the positive effect of the new businesses in Old North Durham. The area’s house prices show stable growth in the past eight years, while a similar area such as East Durham/Edgemont has suffered a decline in house prices. This comes to show that risky investments in bad neighborhoods could pay dividends for residents as well as local business owners. The information in this analysis can be used as an example of how strategic reclamation of industrial spaces can prove an economic boon through enticement of the “alternative lifestyle” market. Basing its actions on the positive changes Old North Durham, the local governments may be able to stimulate the revival of other areas in Durham (as well as other cities with a ample vacant industrial space) by creating incentives for entrepreneurs to develop such spaces.
 Holloway, Carson. Personal Interview. 28 03 2013.
 Wilson, Sean. Personal Interview. 25 03 2013.
 Barrera de Grodski, Leon. Personal Interview. 23 03 2013.
 Holloway, Carson. Personal Interview. 25 03 2013.
 Schwartz, Joe. “Bad blood brewing over Old North Durham Park.” Indyweek.com. N.p., 13 4 2011. Web. 6 Apr 2013. <http://www.indyweek.com/indyweek/bad-blood-brewing-over-old-north-durham-park/Content?oid=2361969>.
By David Wang LR_WangDavid
School Quality and Property Values
The American education system spans communities of extremely diverse populations, across many socio-economic and ethnic lines. It is likely that the successes of students in these communities hinge on the combination of the students’ innate intelligence, living environment, and school quality. Standard models suggest and many have provided empirical evidence that housing prices have a correlation to this school quality. However, since these factors could be interrelated, to isolate and identify the actual effects of school quality on property values requires careful consideration of student, house, and neighborhood-specific attributes. It is also important to note how school quality is measured. With the passage of the No Child Left Behind Act of 2001 and the shift away from using per pupil expenditures as a proxy for the quality of education, attention has been drawn to the use of standardized test scores as the marker of education quality.
Recent literature reconfirms the positive correlation of school performance on house prices, using basic hedonic models with added controls for test scores of local schools. However, in older papers, the authors have difficulties controlling for neighborhood characteristics that are correlated with the test scores and house prices. Black (1999) develops a new method for assessing school quality by using attendance district boundaries to account for neighborhood characteristics. This method allows her to compare school to school differences in test scores with house prices. Crone (2006) uses a model on a full unrestricted sample that allows for testing of house price and test score relationships on both a school and district level. In addition, he adapts Black’s boundary model to allow for this district level analysis. In contrast to Black, Crone argues that it is a district-wide educational quality, not individual school quality that affects house prices. Finally, Clapp, Nanda, and Ross (2007) also consider Black’s model, but instead use a time-based fixed effects model over the period from 1994 to 2004 to control for the neighborhood characteristics. Despite using different methods, all three papers agree that a positive correlation exists between school test scores and housing prices.
Black’s (1999) measurement of differences across attendance district boundaries enables the use of fixed effects in her model. This district boundary is the line that separates the respective attendance areas of schools. This line provides a discrete point at which standardized test scores should change. However, the line may run through continuous neighborhoods, allowing Black to compare any sudden jump in test scores with houses that are situated in similar neighborhoods. By using dummy variables to account specifically for the districts, Black avoids the omitted variable biases of property taxes, public goods, and neighborhood characteristics. Using MEAP testing data from Massachusetts elementary schools, Black focuses on the fourth grade level. Under the basic, unrestricted model, she must control separately for house level characteristics, distance from the CBD, in addition to other school quality characteristics, such as per-pupil expenditures. She finds that per-pupil expenditure is positively correlated with house prices while higher pupil/teachers ratio is negatively correlated with house prices. Nevertheless, the crux of the problem involves the unobservable characteristics of a neighborhood. Black examines different subsets of her data, restricting the samples to houses nearer and nearer the boundary and increasing the probability that the houses on opposite sides of the boundary differ in only the elementary school quality. Her study reveals that if neighborhood characteristics are not carefully controlled, the marginal value of school quality as measured by test scores on housing prices will be overestimated. Black concludes that parents will pay higher house prices for better schools, but does not examine whether there exists a district level effect of school quality on prices.
Newer researchers incorporate Black’s boundary model and conclusions as supplements to their models. However, unlike Black, Crone (2006) argues that home buyers actually value local public education at the district level rather than the neighborhood school level. Using fifth and eleventh grade Pennsylvania System of School Assessment (PSSA) data from Montgomery County, Crone makes findings that differ from Black’s. While Black argues that differences on an individual school basis affect home prices, Crone claims otherwise. Crone argues that for fifth grade test scores, differences are only significant on the district-level. In fact, he finds that fifth grade test scores are better predictors of house prices than eleventh grade scores. Perhaps this discrepancy could be attributed to the location of families with young children and the subsequent lack of relocation as the children grow up. Crone’s differing results could also be due to his use of the full sample rather than a boundary restricted sample. Crone’s more comprehensive dataset allows him to make district level regressions, while Black’s dataset is restricted to individual schools.
Crone’s study also provides additional factors that may affect school quality and thus house prices. For example, class size is not significant at the elementary school level, but it makes a significant difference at the high school level. By incorporating this measurement into the main model, Crone reduces the chance of omitted variable bias from Black’s neighborhood fixed effects model. The neighborhood fixed effects do not account for differences in the schools, such as per-pupil expenditure or class size, of which the latter was not included in Black’s model, which only seeks to explain the impact of school quality differences. As an additional test, Crone uses Black’s boundary method to estimate the effect of both school and district test scores on housing prices. He finds that with this smaller sample, there is no significant coefficient on fifth grade scores, further conflicting with the results given by Black. However, on the high school level, the results become more significant with the smaller sample with boundary dummies. This result differs from the result when controlling for detailed characteristics in the model with a full sample. Finally, Crone’s study also finds that per-pupil expenditures do not affect the house prices above their effect on student test scores or achievement. Overall, Crone brings the conclusion that school district quality should be considered over the quality of individual schools when determining the effect on house prices.
Clapp, Nanda, Ross (2007) introduce a twist on the examination of test scores and housing prices by suggesting that the quality of school districts is a function of both test scores and demographic composition. Since people tend to use the most accessible signals to judge school quality, they often rely on the demographics of a school, which are very visible. Thus, Clapp examines the significance of the test score and demographic composition effects on house prices. Like Black and Crone, Clapp also finds a statistically significant, though very small, effect of test scores on house values. Clapp also agrees with Black’s finding that failing to control for unobservable characteristics in the neighborhood leads to overstatement of the test score effect. However, Clapp extends this argument by also including the effects of race percentages on home prices. He finds that an increase in percent African-American and percent Hispanic leads to a decline in property values. Nevertheless, over this time period, people appear to be placing more importance on test scores and less on demographics when evaluating school quality.
Of the three papers, Clapp’s is the only one to use a time-based fixed effects model. While the studies of Black and Crone use averages of a single three-year period and district boundaries as fixed effects, Clapp instead exploits the cross time variation in the 1994-2004 panel data to separate school attributes from neighborhood quality. Clapp also incorporates additional neighborhood fixed effects by comparing sales occurring in different neighborhoods, but the same school district. This combination of time variation based identification strategy and also neighborhood fixed effects should yield more accurate estimates than either strategy alone. However, one downside to Clapp’s method that does not appear with Black’s or Crone’s is the possibility of unobservable changes over the sample’s long time period.
Through these three papers, we see a wide variety of techniques used to analyze public school test scores and house prices, yet arrive at the conclusion that standardized test scores do impact housing prices. In these papers, however, we assume that district boundaries are fixed and that students must attend schools in their attendance zone. With the rise of charter schools, students no longer are limited to the public schools near their homes. As the population of students going to charter schools increases, we may begin to see a declining importance of neighborhoods under the models discussed in the three papers reviewed here. It may be worthwhile to examine the effects of charter schools on home prices, both in the area of the school and of the students.
Black, Sandra E. “Do Better Schools Matter? Parental Valuation of Elementary Education*.” Quarterly Journal of Economics 114.2 (1999): 577-99. Web. 7 Feb. 2013.
Clapp, John M., Anupam Nanda, and Stephen L. Ross. “Which School Attributes Matter? The Influence of School District Performance and Demographic Composition on Property Values.” Journal of Urban Economics 63.2 (2008): 451-66. Web. 7 Feb. 2013.
Crone, Theodore M. “Capitalization of the Quality of Public Schools: What Do Home Buyers Value?” Working Paper Series, Federal Reserve Bank of Philadelphia (2006): n. pag. Statistical Insight [ProQuest]. Web. 7 Feb. 2013.
By Katerina Valtcheva.
Presentation slides: DP_Valtcheva_Katerina
Linden, Leigh, and Joanh E. Rockoff. 2008. “Estimates of the Impact of Crime Risk on Property Values from Megan’s Laws.”
by Ingrid Zhuang TP_ZHUANGINGRID
Linden, Leigh, and Joanh E. Rockoff. 2008. “Estimates of the Impact of Crime Risk on Property Values from Megan’s Laws.” American Economic Review, 98:3, 1103-1127
Crime rate, victimization, and the fear of crime risk are studied predominantly as local issues. In response to crime risk, residents either vote for anti-crime policies, or they choose to relocate. Therefore, local response to crime is particularly discernible in the housing market, since individuals can reduce their exposure to crime without moving great distances (Linden and Rockoff, 2008). Individuals’ strong distaste for crime, especially sex offenses, indicates an inverse relationship between housing values and proximity to registered sex offenders, as observed by existing studies. Understanding the relationship between property values and local crime risk is important in determining optimal policy decisions, such as proper level of policing and expenditures for programs that reduce crime.
Linden and Rockoff indicate in their paper that previous literature had potential omitted variable bias in both the cross-sectional and the time series models, and crime rates are likely to co-move with other unobserved characteristics in a neighborhood. Linden and Rockoff (2008) improves on past estimates through the use of hedonic estimation methodology to measure the impact of crime risk on property values. They overcome the bias problem by using cross-sectional and time series data on the timing and the exact locations of sex offenders’ arrival based on the implicit assumption that the small neighborhood around a sex offender is relatively homogeneous. The timing of a sex offender’s move-in allows Linden and Rockoff to confirm that the change in property values is not caused by other preexisting shocks.
Three sets of data are analyzed. The first is a January 2005 data on registered sex offenders in Mecklenburg County, North Carolina, provided by the North Carolina Department of Justice (NCDOJ). It contains information on sex offenders’ names, basic characteristics, types of crimes, incarceration dates, addresses of where offenders currently live, and registration dates. The second set of data is collected from the Mecklenburg County division of Property Assessment and Land Record Management, providing information on all real estate parcels in the county and comprehensive physical characteristics for each parcel. The third set is a total of 169,577 property sales of a ten-year period (from January 1994 to December 2004) in the Mecklenburg County. Linden and Rockoff choose to limit the time period to a four-year window: two years prior and two years after the offenders’ arrivals. They match the first dataset with the second dataset to pin down the exact location of registered sex offenders, and merge with the property sales data to exploit the exogenous variation from the move-in to estimate the property value changes.
Assuming living in close proximity to a sex offender has a negative impact on nearby property values, one should expect a fall in prices of homes near the offender’s location subsequent to the offender’s arrival, with the largest impact on homes closest to the offender. The graphical evidence confirms the hypothesis. Comparing to pre-arrival price gradient of distance, figures 2B and 3B exhibit a clear decline for sales during the year within 0.1 miles of the offender. Homes slightly farther away are less affected.
Model and Estimation:
Linden and Rockoff (2008) uses a cross-sectional difference estimator and a difference-in-differences estimator to test the graphical evidence.
where the log of the deflated sale price (sale price/CPI) of the house is a function of a measure of distance from the sex offender, a year specific effect (αt), and a random error term (εijt). D1/10 ijt is an indicator variable: when a property sale occurs within 0.1 miles of a sex offender’s location it equals to one.
Difference-in difference specification:
adds an indicator variable (D3/10ijt) for homes within 0.3 miles of an offender’s address and an interaction of this indicator with an indicator to test whether the sale took place after the offender’s arrival (Postit). αjt is a neighborhood-year fixed effect, Xi observable property characteristics, and the term π1 gives the estimated impact of close proximity to a sex offender on property values.
Cross-sectional difference estimator is used to confirm the absence of preexisting differences in the characteristics of homes located within 0.1 miles of an offender. Table 2 shows two regressions, where Panel A estimates all houses within 0.1 miles of the sex offender’s location that are sold BEFORE his arrival. Panel B has the same estimation but included all houses regardless of whether sold or not, thus they could not estimate the first two columns of Table 2. The insignificance of the results demonstrates a high degree of homogeneity in the data.
Table 3 presents statistical estimates of the impact of a sex offender’s arrival on the nearby housing values. Column 1 shows estimates of equation (1), including sales of all homes in the dataset and sale-year fixed effects. The estimate of the impact (π1, which in this case is simply a measure of the average price difference between houses within 0.1 miles of an offender’s future location and other houses sold within the same year) is approximately 34 percent. Significant at 5 percent level, the estimate confirms that homes closer to offenders’ locations are relatively cheap compare to other parts of the county. Column 2 builds onto column 1 by adding neighborhood-year fixed effects and house characteristics to the regression. The results are not statistically different from zero at any confidence level, implying that control variables in the regression capture almost all of the differences between areas in which offenders move and the rest of the county. In a simple pre-post comparison, column 3 shows estimates of equation (2) without the indicator variables for houses selling between 0.1 and 0.3 miles from the offenders. Linden and Rockoff find that a sex offender’s arrival caused on average 4 percent decline in housing prices within 0.1 miles of an offender’s location, 0.7 percent pre-existing difference in prices. The 3.3% difference is significant at the 10 percent level. Linden and Rockoff’s difference-in-differences specification results are recorded in column 4. The estimates show a slightly higher impact of a sex offender’s arrival, with -4.1 percent at the 4 percent significant level. The impact of offender’s arrival for homes located between 0.1 and 0.3 miles of an offender’s location is curious but statistically insignificant. The result indicates that homes slightly farther away experienced little to no decrease in property values on average. Column 5 re-estimates equation (2), using only property sales from areas with sex offenders’ presence. Instead of controlling for neighborhood-year fixed effects, Linden and Rockoff control offender area-year fixed effects and estimate standard errors clustering at the offender area level. This approach provides better identification by focusing on houses within 0.1 miles of offenders. Since the results are consistent with those from columns 3 and 4, they conclude that additional data from sales outside of offender areas did not bias their estimates. Adding an interaction of the dummy variable indicating a sale within 0.1 miles of an offender after the offender has moved in, with distance from the offender, column 6 shows no changes in results. Robustness and falsification tests are conducted further to attest their conclusion. Due to the focus of this technical presentation, detailed explanations on these results are omitted.
From the hedonic estimations, Linden and Rockoff conclude that houses within a one-tenth mile area around the home of a sex offender fall by 4% on average (about $5,500). The result suggests that residents have a significant distaste for living in close proximity to a known sex offender, and they would be willing to pay a high cost for policies that remove sexual offenders from their neighborhoods. As Linden and Rockoff mentioned in their paper, one possible extension for this study could be adding data on buyer or seller characteristics in order to avoid overestimating or underestimating the average willingness to pay due to the fact that only prices for houses that sell were analyzed. Another contribution would be to explore whether the recession has changed the relationship (comparing pre-recession reaction to post-recession reaction) or examine whether neighborhoods of various demographics will react differently to the presence of sex offenders.
by Bao Tran-Phu
I. Research question
In “Identifying the Impacts of Rail Transit Stations on Residential Property Values,” Bowes and Ihlanfeldt (2001) study the relationship between transit station proximity and local property values. Specifically, they use least-square regressions to study the property value effects of having a Metropolitan Atlanta Rapid Transit Authority (MARTA) rail station nearby. To understand the factors driving this impact, they disaggregate it into four distinct effects: a direct positive effect (from heightened access to transit), a direct negative effect (from physical aspects of the station itself, such as noise and pollution), an indirect positive effect (as stations attract retail development), and an indirect negative effect (as stations serve as a gateway for crime). Previous studies have investigated this transit-property value impact, but by not quantifying the effects of these individual drivers, they have failed to build a comprehensive picture of what exactly causes the impact.
Given a Tiebout amenities-based approach to evaluating the economic benefits of public transit, the benefits (as well as costs) of having a transit station nearby would be fully capitalized in local property values. Thus, by analyzing the relationship between transit station proximity and property values, in theory they are conducting a comprehensive accounting of all of the rail’s social benefits. More importantly, however, they employ regression analysis to better understand the drivers behind this transit-property values link. This study is important for transit policy decisions for two reasons: first, for proposed transit networks, it can help transit authorities choose optimal locations for their stations and to understand what their effects will be on the surrounding neighborhoods. Second, for existing transit networks, it can help local governments tailor policy in order to promote the benefits and mitigate the costs caused by the transit stations.
II. Empirical analysis
This paper employs a series of multivariate least-squares linear regressions. To briefly illustrate the mechanisms behind ordinary least-squares (OLS) regression, assume a response variable Y, where Y is determined by the following relationship:
Y= α0 + α1X1 + α2X2 + α12X1* X2
X1 and X2 are explanatory variables that are hypothesized to collectively explain Y. X1*X2 is an “interaction” of the two explanatory variables, capturing any synergistic effects above and beyond the sum of the two effects individually. Finally, α1, α2, and α12 are coefficients that quantify the linear effects of their respective explanatory variables, while a0 gives the average value for Y when all explanatory values equal zero. Explanatory variables can either be continuous, discrete (for ex, binary), or mixed. If explanatory variables are continuous, then their coefficients give the slope of the variable’s effect, holding fixed all other variables. If they are binary “dummy variables,” then the coefficient quantifies the difference-in-means effect of X=1 (relative to X=0), holding fixed all other variables. From there, we set up a regression, given i observations:
In the estimated regression, ui is the residual, signifying the variation in Ythat is not explained by the regression model. Statistical software can then find the coefficient values that minimize the sum of square residuals. In their paper, Bowes and Ihlandfeldt use three regression models that are estimated separately.
Hedonic price model (OLS regression)
The first model is used is to quantify station distance’s effect on property values, and to disaggregate that effect into the four main effects listed above.
P = α0 + αZZ + βLL + βCC + βRR + γ SS
P is house sale prices, C is neighborhood crime density, R is neighborhood retail employment, S is rail station proximity, and Z and L are sets of other house and neighborhood characteristics respectively that are independent of rail station proximity (for example, the number of rooms in the house). They opt for a semilog regression, (whereby the response and explanatory variables relate exponentially) over a linear regression, and they find that doing so achieves higher statistical significance. Also, station proximity (S) is captured by a set of dummy variables for being less than one-quarter mile, between one-quarter and one-half mile, between one-half and one mile, between one and three miles, and greater than three miles away. They opt for this technique over using a continuous proximity variable based on the intuition that the effects of station proximity will differ depending on how far away the neighborhood is from it to begin with.
Crime and retail employment auxiliary models (random effects regression)
The two auxiliary models are then used to provide a closer look at how exactly crime and retail development indirectly affect property values.
C = ψ0 + θNN + ψSS
R = φ0 + πQQ + φSS
As before, C is crime density, R is retail employment, and S is a set of station proximity dummy variables. N and Q are respective sets of crime- and retail employment-correlated neighborhood factors that are independent of station proximity. In both cases, since they use panel data on 206 census tracts from 1991-1994, random effects models are used instead of the standard OLS (“fixed effects”) regression model. This is a variation of OLS, whereby the census tracts are treated as randomly-selected samples from a large pool, as doing so avoids having to employ 206 dummy variables to control for the tract-level effects.
Each model is analyzed both with and without interactions. Including the interactions allows the authors to study how the effects of station proximity vary depending on station characteristics (for example, whether or not the station has a parking lot), neighborhood income, and distance from the CBD.
III. Results and implications
For the hedonic price model regression, R2 = 0.475, where R2 measures the percentage of the variation in the response variable explained by the regression. Effects of both crime density and retail employment on property values were statistically significant, providing validation for the two auxiliary models. Excluding interactions, being less than one-quarter of a mile away from a station causes property values to be 20% lower than equivalent houses more than 3 miles away, but houses between 1-3 miles away are worth the most (holding all other variables constant). When the interactions are included, Bowes and Ihlanfeldt additionally find that the premium paid for being between 1-3 miles from a station is greater for high-income neighborhoods, and being less than a mile from a station is worth more in neighborhoods further away from the CBD.
For the crime auxiliary model, R2 = 0.824. Without interactions, it is found that being less than half a mile from a rail station led to higher crime density. Including the interactions then shows that rail stations that have parking lots increase crime for neighborhoods less than one-quarter mile away, but decrease crime for neighborhoods between one-quarter and one mile away. This may indicate that station parking lots serve as a magnet for crime, attracting it in the station’s immediate vicinity but pulling it away from areas a bit further away. Also, the authors find an increase in crime as neighborhood median income rises, but only for tracts less than one-quarter mile from the station. Finally, the station proximity effect on crime is smaller for neighborhoods further away from the CBD. This could suggest that in the suburbs, criminals may see themselves as more easily identifiable as an outsider, and the higher perceived probability of being caught could thus deter them.
Finally, for the retail auxiliary model, R2 = 0.670. Excluding interactions, station proximity has no significant effect on retail employment. When the interactions are added, however, it is shown that further away from the CBD (greater than 10 miles away, for example), having a station nearby has a significant positive effect on retail employment.
Overall, these three regression analyses demonstrate that station proximity affects property values both indirectly (by affecting crime and retail development) and directly (possibly by improving transit accessibility and/or by imposing negative physical externalities) (see Figure 1). Direct effects are generally found to be larger than the indirect crime or retail effects. Further, Retail effects are larger in magnitude than crime effects, except for downtown neighborhoods closest to the stations. Most importantly, though, the study demonstrates that the station proximity effect differs greatly depending on neighborhood income, distance from the CBD, and distance from the station. The biggest benefactors of rail stations are neighborhoods that are far from the CBD and very close (one-quarter to one-half mile) from the station, in which case the positive retail effect dominates. The biggest losers are higher-income downtown neighborhoods closest (less than one-quarter mile) to the station, in which case the negative crime and direct externality effects dominate.
Figure 1. The three models demonstrate station proximity’s direct and indirect effects on property values.
In planning public transit, local governments may assume that stations will have a dominantly positive proximity effect on surrounding property values, due to the heightened transit accessibility that the stations provide. As these results show, however, the reality is more complex. To accurately predict the effect of a nearby transit station, the underlying drivers as well as the neighborhood’s characteristics must be considered. Additionally, local governments should seek to maximize the benefits and minimize the costs of existing transit stations. In low-income neighborhoods, governments should focus on mitigating the stations’ negative externalities, while the focus should be on reducing transit-driven crime in high-income neighborhoods.
In this paper, Bowes and Ihlanfedlt are meticulous to separate the competing indirect effects of rail stations driven by crime-related and retail employment-related factors. Curiously, a similar effort is not made to disaggregate the direct effects of station proximity, even while two major competing effects (transit accessibility and negative physical externalities) are identified.
In addition, property values are used here as a proxy for the local demand for transit, as theorized by the amenities view of public goods. However, given that real estate buying decisions are likely temporally and conceptually displaced from household transportation decisions, property values may be only an imprecise proxy. A possible extension could thus be a similar study, replacing property value with household transportation costs as the response variable. This study would investigate the effects of nearby transit stations on actualized transportation costs for surrounding households, which would be rooted in actual transit use. These transportation cost savings could then be aggregated with the other direct and indirect effects studied in this model to create single hedonic model, providing a more complete illustration of the economic impact of public transit.
The full document can be found here: TP_Bao
Bowes, David R., and Keith R. Ihlanfeldt. “Identifying the Impacts of Rail Transit Stations on Residential Property Values.” Journal of Urban Economics 50 (2001): 1-25.