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The Demand for Historic Preservation John I. Carruthers US Department of Housing and Urban Development Office of Policy Development and Research 451 7th Street SW, Rm Washington, DC
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The Demand for Historic Preservation John I. Carruthers US Department of Housing and Urban Development Office of Policy Development and Research 451 7th Street SW, Rm Washington, DC David E. Clark Marquette University Department of Economics Box 1881, WI (corresponding author) Michael Tealdi Former Graduate Student Marquette University Department of Economics Box 1881, WI JEL Classification: R31, Q51 Draft version: Please do not quote without permission The Demand for Historic Preservation Abstract Historic preservation is commonly used to protect old buildings and neighborhoods from deterioration. In 1981, the established a historic preservation commission to develop and maintain a local register of places with historical importance to the area. The commission also reviews all applications for historic status as well as any requests for exterior alterations. As such, there are numerous rules and restrictions that are imposed on property owners once it has been declared a historic site. Thus, while historic designation can serve to internalize the externalities in neighborhoods with historic buildings, it also imposes costs on homeowners who wish to make improvements to their homes. This paper uses a hedonic model to estimate the impact of historic preservation on the sale price of a single family home in the area. Preliminary results show that the impact of historic preservation is positive when it is significant, with the average impact at 26.6%. However, there was significant variation between districts, with the impact significantly positive in 13 of 22 districts used in the sample. Specifically, the positive impact ranged between 11% and 65%, holding other factors constant. None of the 22 districts had a negative and significant impact. An evaluation of spillover effects reveal that just over one third of them displayed positive and signficant spillover effects, whereas 21% had negative and significant spillover effects. The remainder were insignificant. An important question is what factors influence this variability in historic preservation effects. The eventual goal of this research is to extend our preliminary analysis to two stages using a recently developed method that employs spatial econometric methods to solve the unique identification problems inherent in hedonic models (Carruthers and Clark, forthcoming in Journal of Regional Science). This will permit us to determine the specific factors that influence these premiums. While the spatial estimates presented in this preliminary work do not permit a two-stage model, we did explore whether implicit prices appear to be correlated with the household income and racial makeup of the neighborhoods in which they are located. The findings show little evidence that the implicit values of historic districts are correlated, but the implicit price associated with historic district spillovers was positively correlated with both neighborhood measures. 2 The Demand for Historic Preservation 1. Introduction The National Historic Preservation Act was passed by Congress in 1966 and it allowed the Secretary of the Interior to create and maintain a national register of historic places that is comprised of various buildings, sites, and districts that are of historic significance. However, much of the historic preservation that is done in the US is initiated at the local level, where local communities establish their own historic districts. There are a number of reasons to create a local district within a city. First, a district can be used to preserve the character of the neighborhood for current and future generations, and reduce the externalities associated with modifications that are inconsistent with the other homes in the community. Second, many cities have used historic preservation designation as a neighborhood revitalization tool to attract new residents and businesses to an area. Generally speaking, historic districts are thought to have a positive effect on property values, and numerous studies have documented the positive impact of these districts on local home values (Ford, 1989; Coffin, 1989; Asabere and Huffman, 1994b; Clark and Herrin, 1997; Coulsen and Leichenko, 2001; Leichenko, Coulson and Listokin, 2001; and Coulson and Lahr, 2005). However, several studies have documented negative impacts (Asabere and Huffman, 1994a; Asabere, Huffman and Mehdian, 1994), and even among those studies that generally find a positive impact, the size of the premium can vary substantially. Of course, historic preservation does not come without costs. Once a district has been established, the owners must abide by a number of rules and guidelines applying to everything from general maintenance to exterior alterations. In most cases, approval must be obtained from the commission before any work can be performed on the house. It is possible that the costs associated with the additional rules and regulations could outweigh any of the benefits with historic preservation, especially for districts that have only recently been designated. The objective of this paper to study the factors that influence the demand for historic preservation in the area. While the literature on the hedonic impacts of historic preservation focuses on single stage models which examine the impact of historic districts, or proximity to districts on the implicit price function, we extend the hedonic model to two stages as suggested by Rosen (1974) in his original work. However, we recognize the unique identification challenges generated by the hedonic model (e.g., see Brown and Rosen, 1985; Epple, 198x). In a recent paper, Carruthers and Clark (2010) employ the Geographically Weighted Regression (GWR) to derive demand functions for environmental goods in the Seattle area (i.e., King County, WA). While it is our intention to ultimately use that methodology in this study, the estimates presented in this preliminary draft focus primarily on a less complex spatial econometric approach (i.e., spatial 2SLS) in the first stage. 3 2. Hedonic Prices and Implicit Markets Willingness to pay for historic preservation can be estimated either directly using stated preference approaches (e.g., conjoint analysis or contingent valuation methods) or indirectly using revealed preference approaches such as hedonic price analysis. There are two broad forms of hedonic modeling, both of which examine how nonmarket goods are capitalized into local input prices. The intercity hedonic approach derives implicit prices by examining interregional compensating differentials in wages and/or land rents. This technique which was first suggested by Rosen (1979) and more thoroughly developed by Roback (1982) and Blomquist, Berger and Hoehn (1986). The intercity hedonic model has been used to derive implicit values for various nonmarket goods 1 and has been extended to two stages to derive demand for nonmarket goods (Clark and Kahn, 1988, 1989). The intracity hedonic model, in contrast, primarily focuses on the capitalization of local attributes into local housing prices, although intracity wage variations have also been examined (Eberts, 1982). The intracity model builds on the seminal work of Lancaster (1966), and was more formally developed by Rosen (1974) and Freeman (1979). In Rosen s original paper, demand for locational attributes was done in a two-stage process. In the first stage, the transacted housing price is regressed on measures of all of the things that matter to the buyer, including structural features, neighborhood characteristics, and environmental factors that vary by location. This stage is the hedonic price function, and it produces a vector of parameters that can be used to derive marginal implicit prices for each attribute. Then, in the second stage, quantities of the attributes of interest are regressed on their estimated implicit prices, which are endogenous, a set of exogenous demand shifters and the prices of relevant complements and/or substitutes. This stage derives the inverse demand function, and it is needed for recovering the values of non-marginal differences in the quantity consumed and for estimating assorted elasticities of demand. 1 Blomquist, Berger and Hoehn (1986) derived an urban quality of life index, whereas Clark and Nieves (1993) examined implicit values of various types of noxious activities. 4 P h o 3 P(z) o 2 b 3 o 1 b 2 b 1 Attribute z k Figure 1 Offer Function, Bid Function and Hedonic Price Function The hedonic housing price model characterizes housing as a bundle of attributes contained in a vector z, where z (z 1,z 2,...,z k ). These attributes can be related to the structure or the neighborhood. Thus, the equilibrium market price for a given house is dependent on the vector z (i.e., p(z) p(z 1, z 2,...,z k )). As shown in Figure 1, the hedonic price function (P(z)) for attribute z k is a reduced form function that is derived from the interaction of sellers with offer functions (o 1, o 2, o 3 ), and buyers with bid functions (b 1, b 2, b 3 ) in an implicit market. The model assumes that (a) there is perfect information about the bundle; (b) there are no transactions costs associated with the trading of attributes, and (3) there is a continual offering of housing attributes in the housing market. The hedonic price function is believed to be nonlinear since housing is immobile, and cannot be easily repackaged. If these assumptions hold, then the marginal implicit price of any given attribute, z k, is derived as the partial derivative of the hedonic price function with respect to that attribute, or p zk (z) p/ z k. Rosen (1974) was the first to recognize that the derived marginal implicit prices could be used to derive demand functions in a two-stage model. Briefly, a second stage model can regress levels of z k on the implicit price, p (z) in addition to demand shifters, or alternatively, estimate an inverse demand z k function in which the dependent variable is denoted by implicit price. We employ the latter approach here. p z k 0 1 k 2 z x e (1) 5 In the inverse demand function, the implicit price is a function of the level of the characteristic, z k as well as a vector of demand shifters, x. Since equation (1) includes an endogenous variable ( z k ) it must be estimated via an instrumental variables procedure. Rosen (1974) characterized the identification problem as similar to any supply and demand system. If that is the case, one can either assume the supply function is exogenous meaning that the implicit price is demand determined (e.g., Harrison and Rubinfeld, 1978), or it can be assumed to be endogenous (Nelson, 1978) and supply shifters are then employed as instruments to identify the second stage demand functions. However, in the early 1980 s, several studies (e.g., Brown and Rosen, 1982; Palmquist, 1984; Bartik, 1987; Epple, 1987) noted that the hedonic model had a unique form of endogeneity. Specifically, they argue that each implicit price results from a unique interaction between an individual demand and an individual supply function in the hedonic model as shown in Figure 2. Thus, a shift in the implicit supply of attribute z k results in a corresponding shift in the implicit demand for that attribute. Implicit Price, p/ z k S 1 S 2 S3 S 4 implicit price function D 1 D 2 D 3 D 4 Attribute, z Figure 2 Implicit Price Function Thus, the appropriate alternative approach is to either impose functional form restrictions on the hedonic function (Chattopadhyay, 1999) or use data from multiple housing markets so as to generate inter-market variation in implicit prices (Epple (1988), Bartik (1987), Brown and Rosen (1982), Palmquist (1984)). Multiple market studies routinely employ data from different cities (Zabel and Kiel, 2000; Brasington and Hite, 2005), but Carruthers and Clark (2010) show that the spatial variation of submarkets within a single city can be used to solve endogeneity problem of the two stage hedonic model. Specifically, Carruthers 6 and Clark use Geographically Weighted Regression (GWR) to derive the necessary spatial variation in implicit prices to derive second stage implicit demand functions. Although GWR will be used to apply the approach outlined above to derive the demand for historic preservation, we are only in the early stages of generating GWR estimates 2. Thus, in this preliminary version of the paper, we provide first stage hedonic regression estimates using a spatial lag model to determine first stage estimates. While this precludes the development of 2 nd stage inverse demand estimates at this point, it does provide insights as to how the influence of historic preservation impacts properties in, and how those impacts differ across districts. The spatial lag model is appropriate at this early stage of analysis, because housing markets are subject to a high degree of spatial dependence (Kim et al. 2003; Theebe 2004; Anselin and LeGallo 2006; Brasington and Hite 2005). On the supply side, homes in close proximity are often structurally similar. Likewise, on the demand side, homebuyers regularly emulate one another s behavior. The result is a process of spatial interaction among market participants, which, at a minimum, suggests that the first stage hedonic price function shown in equation (2) should be modified to include a spatial lag of its dependent variable (Anselin 1988; Anselin and Bera 1998). The spatial lag model is specified as: ln( Pi ) 0 Wij p i z i i (2) ~ Where W ij p represents the spatial lag of the dependent variable ( W ij, j i, is a row-standardized n n weights matrix describing the connectivity of observations) giving the average sales price of nearby homes; and is an estimable spatial autoregressive parameter. Because the behavioral underpinning of equation (3) says that the sales prices of nearby homes influence one another, W ij p is endogenous to p i and the function cannot be properly estimated using ordinary least squares (OLS). A viable alternative, is a spatial two-stage least squares (S2SLS) estimation strategy developed by Kelejian and Prucha (1998), which, in a nutshell, involves regressing the spatially lagged variable on all explanatory variables plus spatial lags of those same variables to produce predicted values, and then using those predicted values in place of the actual values in equation (3). Like maximum likelihood estimation, S2SLS yields efficient, unbiased parameter estimates, even in the presence of spatial error dependence (Das et al. 2003). In the 2 GWR involves calibrating a separate regression centered on the location of every single observation in the dataset and, at the location of each regression, information from other locations is discounted with distance from it, so that closer observations have a greater influence on the model s solution. The technique is computationally complex and the output is extensive, consisting of a total of n k parameters, so, in the case of our more simplistic model, there are case, 602,028 unique estimates (i.e., 21,501*28). In addition, the estimation process is complicated by the fact that some historic districts that are far away from a given property fall out of that property s individual sample. Thus, time did not permit an application of GWR to this problem. 7 context of the present discussion, the spatial lag in equation (3) works like a flexible fixed effect, absorbing unobserved spatial correlation in the structure of supply and/or demand. 3. Historic Preservation Literature Most of the earliest studies related to historic preservation utilized a difference-in-difference approach. With this approach, property values within a district are compared to those in non-designated areas. However, the major shortfall of this method is that it only considers changes in the average prices of the properties evaluated. It does not control for other factors that could influence the price of the house, such as neighborhood or property characteristics. In order to overcome this shortcoming, most studies now use a hedonic approach to estimate the impact on property values. These hedonic studies have produced some mixed results, with some showing that having a historic designation actually has a negative impact on property values. The list of requirements to designate and maintain a historical property is rather extensive. Therefore, it is theoretically possible that the regulations imposed by the historical preservation committee may outweigh any potential benefits of having it designated as a historic site. Asabere, Huffman, and Mehdian (1994) observed this in their study of small, historic apartment buildings in Philadelphia. They found that these historic apartment buildings were selling for less compared to properties that were not locally certified. And since there was no statistical difference between federal and local historic districts, they concluded that the guidelines set forward in Philadelphia are too restrictive, thus leading to a decrease in property values. A similar result was found by Asabere and Huffman (1994a) in their study of residential condominium sales in Philadelphia. In this study, they examined the impact of historic façade easements on the property value. Historic façade easements are grants by the owners of historic properties that are used to preserve the outside appearance of the structure. The owner typically receives a federal income tax deduction. However, any subsequent owners are left with the restrictions of the façade easement and without a tax deduction. Therefore, properties with prior grants of historic façade easements sell at a discount compared to other properties. Other studies have shown that historic designation increases property values. One of the earliest studies to use a hedonic approach to estimate the impact of historic preservation on home prices was done by Ford (1989). Ford studied local historic districts in Baltimore, Maryland and concluded that historic districts have higher prices than other similar properties not located in a historic district. Coffin (1989) did a study of two Chicago suburbs, Elgin and Aurora. Aurora established a historic district in 1984 and Elgin established a historic district in The difference between these two cities is that Aurora has an ordinance governing land use within the historic district, while Elgin does not. Coffin found that historic designation did increase property values in the area, but it was not statistically significant in Elgin. The 8 differences between the two cities could not be simply explained by the lack of an ordinance in Elgin. Coffin attributes the differences to better quality information being conveyed to the citizens of Aurora compared to Elgin. Asabere and Huffman (1994b) found that owner-occupied homes in Philadelphia located in a federally certified historic district sold at a premium even though the houses did not qualify for rehabilitation investment tax credits. This implies that the premium can be attributed to the location in the historic district. Clark and Herrin (1997) also found that property values were higher on average in historic districts in their study of Sacramento, California. Similarly, Coulson and Leichenko (2001) find that the benefits associated with historical designation in Abilene, Texas far outweigh any of the costs. More recently, Leichenko, Coulson, and Listokin (2001) expanded upon the previous literature by developing their model on a sample of nine cities within Texas. All studies to this point have been made on a sample within a specific city. The authors argue that the conclusions drawn from these studies are made on too narrow of a sample. However, just like many studies before, the authors conclude that historic designation has a positive impact on property values. Finally, Coulson and Lahr (2005) analyzed appreciation rates across neighborhoods in Memphis, Tennessee. They argue that by using appreciation rates, one can avoid some
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