Measuring the effects of mixed land uses on housing values

Regional Science and Urban Economics 34 (2004) 663 – 680 www.elsevier.com/locate/econbase

Measuring the effects of mixed land uses on housing values Yan Song a,*, Gerrit-Jan Knaap b a

Department of City and Regional Planning, University of North Carolina, Chapel Hill, NC 27599-3140, USA b National Center for Smart Growth Research and Education, University of Maryland, College Park, MD 20742, USA Accepted 11 February 2004 Available online 18 May 2004

Abstract Mixing land uses has become one of the key planning principles of the Smart Growth movement and other land use planning strategies. This article analyzes the impact on the prices of single family houses when mixed land uses are included in neighborhoods. We first develop several quantitative measures of mixed land uses through the use of Geographic Information System (GIS) data and compute these measures for various neighborhoods in Washington County, OR. We then incorporate those measures in a hedonic price analysis. We conclude from this research that housing prices increase with their proximity to—or with increasing amount of—public parks or neighborhood commercial land uses. We also find, however, that housing prices are higher in neighborhoods dominated by singlefamily residential land use, where non-residential land uses were evenly distributed, and where more service jobs are available. Finally, we find that housing prices tended to fall with proximity to multifamily residential units. D 2004 Elsevier B.V. All rights reserved. JEL classification: R14; R31 Keywords: Mixed land use; Job-residents balance; Housing price; Hedonic price analysis

1. Introduction 1.1. Why mixed land use Over the last decade, mixing land uses has become one of the key planning principles among contemporary planning strategies. ‘‘Mix land uses,’’ for example, is one of the 10 * Corresponding author. Tel.: +1-919-962-4761; fax: +1-919-962-5206. E-mail address: [email protected] (Y. Song). 0166-0462/$ - see front matter D 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.regsciurbeco.2004.02.003

664

Y. Song, G.-J. Knaap / Regional Science and Urban Economics 34 (2004) 663–680

principles of Smart Growth promoted by the Smart Growth Network established under the auspices of the U.S. Environmental Protection Agency. The Congress of New Urbanism (CNU) also calls for: ‘‘Neighborhoods [to] contain a mix of shops, offices, apartments, and homes; land uses are mixed—use within neighborhoods, within blocks, and within buildings’’ (CNU, 2002). The call for mixed land uses is a response to a set of complex problems brought on by urban sprawl that have beset most US metropolitan areas. Since the 1960s, zoning ordinances throughout the United States have had the effect of isolating employment, shopping and services from residential housing. As a result, residential neighborhoods have been developed at a substantial distances from jobs and services. Advocates for mixed land uses have argued that the practice of separating land uses has led to excessive commute times, traffic congestion, air pollution, inefficient energy consumption, loss of open space and habitat, inequitable distribution of economic resources, job housing imbalance, and loss of sense of community (Smart Communities Network, 2002). Mixed land use has been considered one of the antidotes to the problems brought by urban sprawl. It is argued that greater mixture of complimentary land use types, which may include housing, retail, offices, commercial services, industrial and civic uses, can be beneficial since it can promote transit-supportive development, preserve open space and other landscape amenities, facilitate a more economic arrangement of land uses, encourage street activity to support retail businesses, help achieve regional housing and employment targets, reinforce streets as public spaces, encourage pedestrian and bicycle travel, and thereby create a sense of community (American Planning Association, 1998). 1.2. Previous studies on mixed land use and property values Despite the interest and claims of the advocates of these mixed land use communities, there is little information about the effects mixed uses have on housing prices. Little is known about the suitable balance between different types of mixed land uses, or between jobs and residents within neighborhoods. To advance the planning debate on mixed land use, we offer in this article a closer look at how the mixture of land uses is valued in the marketplace. Surprisingly, there have been few substantive analyses to show how consumers feel about efforts to mix land uses or balance jobs with residential housing within their communities. The findings from the few studies that attempt to include types of surrounding land uses are inconclusive. Irwin (2002) and Irwin and Bockstael (2001) found a premium associated with permanently preserved open space. Earlier articles have explored the effects of other non-residential uses, such as commercial, office, industry and multi-family residential, on single-family property values. None of those articles, however, have developed and incorporated advanced measures of mixed land use using Geographic Information System (GIS) techniques. Cao and Cory (1981) examined the effect of proximity to non-residential land—uses on residential property values. They constructed a theoretical model of consumer behavior and tested the model, using data from Tuscon, AZ. Their results indicated that the effect of non-residential activity on property value is a priori indeterminate and depends on the

Y. Song, G.-J. Knaap / Regional Science and Urban Economics 34 (2004) 663–680

665

relative strength of positive and negative external effects generated. The empirical test also showed that over low ranges, increasing amounts of economic activity lead to higher surrounding property values. They thus concluded that an optimal mix of land use activities should be sought in locating economic activities in neighborhoods. Grether and Mieszkowski (1980) employed data from 16 market experiments in the New Haven, Connecticut, metropolitan area. They measured the effects of nonresidential land uses, such as industry, commercial, high-density dwellings, and highways, on the prices of nearby dwellings. No systematic relationship between nonresidential land use per se and housing prices was found. Li and Brown (1980) tested the impacts of three types of microneighborhood variables (aesthetic attributes, pollution levels, and proximity) on housing values. Their findings suggested that housing prices rose due to accessibility, but fell due to problems such as congestion, pollution or unsightliness. A study by Geoghegan et al. (1997) is probably the only one that examined the effects of the pattern of surrounding land uses on the value of residential land. They employed data from the central Maryland region and developed spatial landscape indices such as diversity and fragmentation of land uses. The results from their hedonic price model indicated that the marginal contribution to selling price of increased diversity and fragmentation changes in different landscape settings. For example, in the highly developed suburbs of Washington, DC, diversity and fragmentation of land uses are valued positively since diverse and fragmented land uses result in amenities such as walkable access to small shopping areas and public infrastructures such as schools. In sum, no studies have provided a detailed analysis of the effects of various land uses on property values. We attempt to do so here. We proceed using the tools of GIS to develop several quantitative measures of mixed land uses. The inclusion of these GIS-developed land use measures distinguishes this study from previous studies. We then tie these measures to residential property values. In essence, our approach advances knowledge both in the measurement of mixed land uses and in the identification of those features for which homeowners are willing to pay in the marketplace. This model is of Washington County, the western portion of the Portland metropolitan area. Our study area, the most rapidly growing of the three counties in the Portland, Oregon, metropolitan area, contains the cities of Beaverton, Hillsboro, Tigard, Sherwood, Tualatin, King City, Cornelius, Forest Grove, and Durham (see Fig. 1). In the analysis that follows, we consider seven sets of characteristics that affect the value of single-family homes: (1) physical housing attributes; (2) public service levels; (3) location; (4) amenities and disamenities; (5) socio-economic characteristics; (6) neighborhood design features; and (7) mixed land use measures in the neighborhood. By isolating the effects on price of these variables, we can estimate the contributions to single family home values of mixing land uses. We find that housing prices increase with proximity to—or with increasing amounts of—public parks or neighborhood commercial land uses. Housing prices also increase if single-family housing is the dominant form of land use in the community, if non-residential land uses are evenly distributed in the neighborhood, or if there are more service jobs available in the neighborhood. Housing prices fall, however, with proximity to multi-family residential units.

666

Y. Song, G.-J. Knaap / Regional Science and Urban Economics 34 (2004) 663–680

Fig. 1. Portland metropolitan area and study area.

1.3. Research context Mixed land use development has become a popular planning strategy in many metropolitan areas in the US—numerous state and local organizations have adopted planning, regulatory and fiscal reforms and program innovations to encourage mixed land uses. These reforms include changing zoning codes and land use development practices (Victoria Transport Policy Institute, 2003). In this article, we use data from Portland, OR, to explore the practice of mixing land uses and its effect on the price of single-family housing. In 1991, Metro, the regional government in the Portland area, began work on its 2040 Growth Concept. One key component of the growth concept was the designation of mixed use regional and town centers (Calthorpe, 2000; Katz, 1994; Metro, 1992). To implement the growth concept, the Urban Growth Functional Plan was approved in 1997. The Functional Plan specified binding targets and performance measures for each of the subordinate cities and counties (Metro, 2001). Cities and counties are required to change their comprehensive plans to assure that local plans comply with the Functional Plan. Seeking a better balance between jobs, housing and urban amenities around the region, the Functional Plan promotes mixed-use neighborhoods, and encourages each jurisdiction to adopt a unique mix of commercial, retail, cultural and recreational opportunities and to provide mixing types of employment as well (Metro, 2001). Metro has also developed a ‘‘Livable Communities Workbook’’ to

Y. Song, G.-J. Knaap / Regional Science and Urban Economics 34 (2004) 663–680

667

implement the 2040 Growth Concept and to address issues regarding mixed use areas. The Workbook provides a guide to the 27 cities and counties in the metropolitan area for drafting new land-use codes or modifying existing ones (Metro, 2002). In response, local cities and communities have designated some sub-areas to accommodate mixed land uses that reflect a combination of low to high density residential, offices, commercial uses, industrial uses, and institutional uses (City Council of Beaverton, 1999; City Council of Hillsboro, 2000a,b). Perhaps more than in any other metropolitan area, the plans of regional and local governments in the Portland metropolitan area have promoted mixed land uses and job-housing balance.

2. Data, GIS measures of mixed land use, and control variables 2.1. Data Our data come from five primary sources: (1) The tax assessment files from Washington County; (2) Sale transaction files from Washington County; (3) The Regional Land Information System (RLIS) from Portland, Metro; (4) Census data from the U.S. Census Bureau; and (5) Employment data for each Traffic Analysis Zone (TAZ) from Census Transportation Planning Package (CTPP). We include only those single family residential properties sold in the year 2000. Prior to estimation, invalid transactions and multiple sales were omitted to ensure that sales reflect market clearing prices.1 The cleaned dataset contains 4314 real estate sale transactions in our study area. The average sale price in this period is US$187,095, with prices ranging from US$68,000 to US$800,000. 2.2. Developing quantitative measures of mixed land use To begin our analysis, we define five types of measures of mixed land use. Mixed non-residential land uses include: (1) neighborhood commercial stores,2 (2) multifamily residential units, (3) light industrial sites, (4) public institutions, and (5) public parks. We first divide the study area into 225 neighborhoods based on Traffic Analysis Zone (TAZ) boundaries. A Traffic Analysis Zone (TAZ) is a special area delineated by state and/or local transportation officials for tabulating traffic related data—especially

1 Following Eppli and Tu (1999), we removed non-arms-length transactions and prevent coding errors based on the ratio of sale price to assessed value. Transactions that have a sale price that is 60 percent greater than the assessed value or that is less than 60% of the assessed value are deleted from the data set. In addition, properties with lots greater than two acres, or age older than 80 years are excluded to maintain a homogenous pool of transactions. Furthermore, we removed the transactions if their assessed value of the land is less than US$1.00 per square foot or the assessed value of the improvements is less than US$25.00 per square foot. 2 In this study, we distinguish between neighborhood commercial and central/general commercial. We also tested the effects of central/general commercial land use on property value and we found discounts associated with being close to central/general commercial uses, or increasing amount of central/general commercial land uses within a neighborhood.

668

Y. Song, G.-J. Knaap / Regional Science and Urban Economics 34 (2004) 663–680

journey-to-work and place-of-work statistics.3 For each TAZ, we computed the following measures:4 MIX_I: Here we offer five measures of accessibility to the nearest nonresidential uses.5 COMDIS—distance from the house to nearest neighborhood commercial use. Most previous studies employed only distance to Central Business Districts (CBD) or major commercial activity centers to measure accessibility to commercial uses. Here we identified all neighborhood commercial stores and computed distances from each house to nearest neighborhood store. These GIS-based distance measures thus measure accessibility more accurately. MFRDIS—distance from the house to nearest multi-family use. PUBDIS—distance from the house to nearest public institutional use. INDDIS—distance from the house to nearest industrial use. PARKDIS—distance from the house to nearest public park. These distance measures only capture the spatial proximity, while the scale of nonresidential uses could have a major influence on property values (Geoghegan et al., 1997). We thus attempt to capture the effects of scale through our MIX_II measures: MIX_II: Here we offer five measures based on proportions of each non-residential land use within a neighborhood. COM—percentage of neighborhood commercial land use within a TAZ. MFR—percentage of multi-family residential land use within a TAZ. PUBLIC—percentage of public institutional land use within a TAZ. IND—percentage of industrial land use within a TAZ. PARK—percentage of public parks within a TAZ. In addition to the mixture of land uses, consumers might also value the pattern of surrounding land uses. Pattern is characterized by different composition of land uses (Geoghegan et al., 1997). We thus develop MIX_III measures to capture the diversity of surrounding land uses: MIX_III: This set of variables is based on the concept of entropy—a measure of variation, dispersion or diversity (Turner et al., 2001). Two variables are developed 3 We do not argue that TAZs are the best aerial unit for measuring mixed land uses. We only claim that it is a convenient unit that illustrates the effects we seek to capture. It has been argued that a buffer surrounding each housing transaction is a better ‘‘neighborhood’’ for the house. However, the scale of the buffer is under debate and thus demanding much more exploration. In this project, since employment data are based on TAZs, we chose to use TAZs. 4 All the calculations were computed using ARCinfo and ArcView with data from Metro’s Regional Land Information System. 5 Generally, commercial, multi-family residential, industrial and public land uses are located closer to major arterial roads for better accessibility; we therefore include distance to major arterial roads (MRJRDDIS) as one of control variables.

Y. Song, G.-J. Knaap / Regional Science and Urban Economics 34 (2004) 663–680

669

to measure land use diversity within a TAZ. The first measure includes single family residential use and captures the overall mixture of land uses. The second measure excludes single family use and captures the mixture of land uses in the nonresidential sector. For both measures, a higher value indicates more evenly distributed land use. P6  ðp Þlnðpi Þ i¼1 i where H1=diversity including Single LUMIX—a diversity index H1 ¼ lnðsÞ Family Residential (SFR), pi=proportions of each of the six land use types such as SFR, MFR, Commercial, Industrial, Public institutional and Park uses, and s=the number of land uses. In this case, s=6. Ps  ðp Þlnðpi Þ i¼1 i NRMIX—a diversity index H2 ¼ where H2=diversity excluding SFR, lnðsÞ pi=proportions of each of the five land use types such as MFR, Commercial, Industrial, Public institutional and Park uses, and s=the number of land uses. In this case, s=5. MIX_IV: This set of variables is developed to represent relative balance between jobs and population within each TAZ. JOB—ratio of total jobs to residents in each TAZ; SERVJOB—ratio of service jobs to residents in each TAZ. Service jobs are defined as population-serving jobs including retail, personal services, entertainment, health, education, and other professional and related services. 2.3. Control variables To control for the influence on sale price of other factors we include six additional sets of variables. A variety of physical housing attributes are included. These include lot size (LOTSIZE), square feet of floor space (FLOORSPACE), age of the house (AGE), and age of the house squared (AGESQUARE). Public services within the neighborhood affect housing values, we therefore include four variables: a binary variable indicating if the house is located within an incorporated municipality and has access to municipal services (INCITY), mean SAT score (SAT), student/teacher ratio of the pertinent school district (STU/TEA); and the adjusted property tax rate (TAXRT). According to economic theory, location with respect to employment centers is a fundamental determinant of location rents. To capture these effects we include measures of distance to three central business districts: Portland (PORTCBD), Hillsboro (HILLCBD), and Beaverton (BEAVCBD). To capture the fact that some households are willing to pay a premium for improved access to major transportation routes, we include distance to major arterial roads (MRJRDDIS). Amenities and disamenities have direct effects on resident utilities and thus can also affect property values. To capture the effects of amenities we include: actual area of golf courses in the neighborhood divided by number of housing units in a neighborhood (GOLF), a dummy variable indicating whether the property is within 150 ft of bodies of water (ONWATER), a dummy variable indicating whether the property has a mountain view (MOUNTNVW), and distance in feet to the nearest minor road (MINRDDIS), where minor roads include collector roads. To capture the effects of disamenities we include measures of exposure to traffic, specified as within 150 ft of major arterial roads (ONMAJRD).

670

Y. Song, G.-J. Knaap / Regional Science and Urban Economics 34 (2004) 663–680

The socio-economic characteristics of households within the neighborhoods are hypothesized to influence housing prices. Thus we include: percent of the population that is white (PCTWHITE) and median household income (MEDINC). In addition, several variables related to neighborhood design are hypothesized to influence price (Song and Knaap, 2003). We include: internal connectivity (INTCONN) and external connectivity (EXTCONN) to reflect qualities in street network design and circulation.6 We include two measures of density: single-family residential dwelling unit density (SFRDNSTY) measured by number of dwelling units divided by the residential area of the neighborhood, and population density (POPDNSTY) measured by number of households divided by area of the neighborhood. We also include pedestrian accessibility to commercial uses (PEDCOM) and bus stops (PEDBUS) in the model: these are measured as the percentage of single family homes that are within one-quarter mile network distance of commercial uses and bus stops. Summary statistics for the dependent variable and all independent variables are provided in Table 1.

3. Hedonic price analysis 3.1. The hedonic model To explore the effects of mixed land uses on property values we use a standard hedonic price model including all variables mentioned above. As semi-log is a common form of such a model, we specify the dependent variable as the log of sale price. We then have: ln(Sale_Price)=a+biXi+diMIXi+ei where a is the constant, bi and di are coefficients, Xi are control variables, and MIXi stand for one set of mixed land use measures developed above. 3.2. The identification problem In the model which includes MIX_II variables, ordinary least squares (OLS) estimates fails to incorporate the fact that the amount of various land uses is endogenously determined. To illustrate this issue, consider the following model which assumes that the price of house i in period t is given by Pit ¼ f ðXit ; MIXIIit Þ þ eit

ð1Þ

where Xit is a vector of control variables associated with house i, MIX_IIit includes variables that capture the proportions of non-residential land uses, such as neighborhood

6 For more on measures of neighborhood design, please see Song and Knaap (2004). Our measures of connectivity involve the number of street nodes and segments, and the distance between points of access into the neighborhood. Internal connectivity is defined by the number of street segments divided by number of street nodes; the greater the INTCONN, the greater the internal connectivity. External connectivity is defined by the median distance between Ingress/Egress (access) points in feet; the greater the distance, the poorer the external connectivity. Street segments are defined by the part of the street between two street nodes. Street nodes are defined as street intersections, T-sections and street dead ends.

Y. Song, G.-J. Knaap / Regional Science and Urban Economics 34 (2004) 663–680

671

Table 1 Summary statistics for all variables Variable

Unit of measures

Mean

Standard deviation

Minimum

Maximum

Dependent variables SALE_PRICE LOGSALEPRICE

Dollar Log(dollar)

187,095 12.09

52,782 0.31

68,000 11.13

800,000 13.59

5261.62 662.69 17.18 1081.18

250.58 456.00 1.00 1.00

82,710.22 7130.00 81.00 6561.00

0.50 13.67 0.36 0.96

0.00 501.00 35.41 13.69

1.00 548.27 36.98 18.73

19,382.66 16,281.01 18,195.82 1746.77 39.87

680.60 967.89 17,270.07 44.41 3.18

82,660.94 84,729.16 113,661.83 10,757.09 715.12

0.25 1556.61 0.24

0.00 0.00 0.00

1.00 28879.63 1.00

0.05 10,163.69

0.54 16,900

0.10 128.14 1.79 1.16 0.21 0.26

0.17 189.86 0.07 1.60 0.00 0.00

1.00 1116.80 41.30 10.32 1.00 1.00

971.62 679.80 1514.35 296.43 623.69 0.07 0.07 0.03 0.08 0.06 0.18 0.23 0.13 0.16

79.19 61.12 67.08 2.13 36.30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

5379.45 4076.69 8243.39 1905.84 4183.17 0.69 0.45 0.22 0.46 0.28 0.95 0.99 0.92 0.89

Independent variables (1) Property Physical Structural Characteristics: LOTSIZE Square feet 8649.59 FLOORSPACE Square feet 1783.78 AGE Year 22.31 AGESQUARE Year 792.63 (2) Public Sector Characteristics: INCITY Binary 0.56 SAT Score 537.77 STU/TEA Percentage 36.62 TAXRT Milrate 15.90 (3) Accessibility Characteristics: HILLCBD Feet 41,386.13 BEAVCBD Feet 25,892.10 PORTCBD Feet 50,507.95 MARRDDIS Feet 2083.47 MINRDDIS Feet 85.26 (4) Amenity Characteristics: ONMAJRD NA 0.07 GOLF Square feet 225.47 MOUNVIEW Binary 0.06 (5) Neighborhood Socio-Economic Characteristics: PCTWHITE Percentage 0.93 MEDINC Dollar 40,877.17 (6) Neighborhood Design Features: INTCONN NA 0.62 EXTCONN Feet 379.22 POPDNSTY # of People/acre 2.28 SFRDNSTY # of HHunit/acre 4.48 PEDCOM Percentage 0.26 PEDBUS Percentage 0.38 (7) Mixed land uses variables: COMDIS Feet 1719.83 MFRDIS Feet 1164.53 INDDIS Feet 2814.64 PUBDIS Feet 492.78 PARKDIS Feet 997.63 COM Percentage 0.05 MFR Percentage 0.06 IND Percentage 0.01 PUB Percentage 0.12 PARK Percentage 0.11 LUMIX Proportion 0.51 NRMIX Proportion 0.54 JOB Proportion 0.31 SERVJOB Proportion 0.25

Definitions of these variables are provided in Section 2.2 and 2.3 in the main text.

1 76,093

672

Y. Song, G.-J. Knaap / Regional Science and Urban Economics 34 (2004) 663–680

commercial, light industrial, public institutional, multi-family residential, and public park land uses, around house i in period t, and eit is the unobserved term. The identification problem exists if the amount of surrounding non-residential land uses, MIX_IIit, depends on the price of surrounding single family houses. For example, commercial land uses might be located in neighborhoods where houses are sold at higher prices.7 Therefore we have MIXIIit ¼ gðPjt ; Dit Þ þ git

ð2Þ

where Pjt are prices of surrounding single family houses, Dit include variables that affect the decision of developing that non-residential use, and git is the unobserved error term. If this is true, MIX_IIit variables are endogenous and thereby correlated with the error term. To investigate this problem, we compare results from OLS and instrumental variables (IV) approaches and present the results below.

4. Results8 Table 2 reports the results of the variables measuring mixing land uses from the OLS and IV regressions for a semi-log formulation of the hedonic equation. The estimated effects of mixed land use variables on residential property values are of primary interest. Discussion of the performance of control variables is provided in Appendix A. 4.1. Model 1 with MIX_I variables The results of Model 1 are within expectation. We found that price is increasing with distance from multi-family residential units. On the other hand, price is increasing with proximity to a public park, and a neighborhood commercial area. Distances to industrial and public institutional land uses have insignificant effects on price. 4.2. Model 2 with OLS estimates of MIX_II variables The results of Model 2 are surprising. The results show that the percentage of park area has a positive effect on sale price. Other variables—proportions of neighborhood commer-

7 The same concern might apply to the variable of distance to nearest commercial use (COMDIS). We have performed Hausman command to test for endogeneity and found COMDIS is not endogenous. 8 We have also performed the exercise of incorporating spatial effects into our hedonic price structure explicitly. The results indicated the presence of spatial error dependence. In other words, the error term at each location is correlated with values for the error term at other locations. This might be due to the two following reasons. First, measurement errors are likely to exist and to spill over across the spatial units since some data are collected and constructed at an aggregate scale. Second, some of the omitted variables, such as number of rooms, exterior material of the house and other house physical characteristics, are correlated and this might be another cause for the presence of spatial error dependence. In the presence of spatial error dependence and spatial heteroskedasticity, OLS estimates are still unbiased (Anselin, 1988). The parameter estimates for other variables will be inefficient, but this inefficiency is not of major concern because of large number of observations (Irwin and Bockstael, 2001).

Y. Song, G.-J. Knaap / Regional Science and Urban Economics 34 (2004) 663–680

673

Table 2 Regression results of variables measuring mixing land uses Variable

Model 1 (MIX_I)

COMDIS

0.000011*** (0.00000) 0.000013* (0.00003) 0.000006*** (0.00000) 0.0000059 (0.00000) 0.0000014 (0.00000)

PARKDIS MFRDIS INDDIS PUBDIS COM MFR IND PUB PARK

Model 2 (MIX_II) OLS

Model 3 (MIX_II) IV

0.018860 (0.00564) 0.010320 (0.03534) 0.117000 (0.05537) 0.006770 (0.02826) 0.003958* (0.00823)

0.004858** (0.00488) 0.016116 (0.03529) 0.095689 (0.05568) 0.005461 (0.02859) 0.003868* (0.00876)

Model 4 (MIX_III)

0.019340* (0.01901) 0.052920* (0.01332)

LUMIX NRMIX JOB SERVJOB R SQUARE

Model 5 (MIX_IV)

0.7488

0.7486

0.7018

0.7494

0.007650 (0.00618) 0.004300* (0.002539) 0.7498

*, **, and *** indicate significance level at the 0.001, 0.005 and 0.05 levels, separately. Standard errors are shown in parenthesis. Results of other control variables are shown in Appendix A.

cial, public, multi-family residential and industrial land uses are insignificant. However, as mentioned before, we suspect that identification problems might exist and thus result in inconsistent OLS estimates. 4.3. Model 3 with instrumental variable (IV) estimates of MIX_II variables To address potential problems of endogeneity, Model 3 incorporates an instrumental variables approach. There is a strong likelihood that there is a correlation between MIX_II variables and the residuals in the model. This correlation violates one of the basic assumptions of independence in OLS regressions. We first test this possibility through a Hausman endogeneity test and find the differences between the IV estimates and OLS estimates are large enough to suggest that the OLS estimates are inconsistent.

674

Y. Song, G.-J. Knaap / Regional Science and Urban Economics 34 (2004) 663–680

We then test to see if the reason for the inconsistent estimates is due to the endogeneity of percentage of neighborhood commercial (COM).9 We found that the Hausman statistics is 47.07 (chi-square) and is significant at the 0.0000 level. The small p-value indicates that there is a significant difference between the IV and OLS coefficients, and OLS is not consistent. We therefore adopt an instrumental variables approach in which instruments are used to predict COM, which is treated as an endogenous variable. Thus we have Pit ¼ f ðXit ; COM*; Other MIX IIit VariablesÞ þ eit 10

ð3Þ

Combined with Eq. (2), we now have the situation in which Xit, other MIX_IIit variables, and Dit are exogenous and are instruments used to obtain predicted COM (COM*). As mentioned before, Dit is a vector of variables influencing the decision of the development of neighborhood commercial areas. In identifying appropriate instruments for COM, we seek variables that are correlated with COM but uncorrelated with the error. Specifically, we use variables indicating the parcel’s distance to central general commercial use, as well as the proportion of the perimeter of the neighborhood commercial lot that is facing a major road. These two variables are exogenous but they are correlated with COM as they potentially influence the decision of the development of neighborhood commercial stores. The area devoted to neighborhood commercial land uses in a neighborhood, to a large degree, is affected by the distance from the houses in the neighborhood to the nearest central commercial shop. Apparently, if there is a sizable general commercial store nearby, there would not be an adequate market for small stores in the neighborhoods. It is probable that the proportion of the perimeter of the neighborhood commercial lot that is facing a major road, a proxy for visibility of a neighborhood store, correlates with the amount of neighborhood commercial land uses. We see from Table 2 that the OLS and IV estimates are very similar with the exception of the coefficients associated with the neighborhood commercial variable. Whereas the OLS estimate of this coefficient is insignificant, the IV estimate is positive and significant at the 0.5% level. Results from IV also show that percentage of park has a positive effect on sale price. 4.4. Model 4 with MIX_III variables The results of Model 4 produced interesting results. The negative sign of LUMIX indicates that price is increasing if single-family residential land use is the dominant use in the neighborhood. The positive sign of NRMIX further shows that price is increasing if the other land uses such as multi-family residential, light industrial, public institutional and neighborhood commercial land uses are distributed evenly in the neighborhoods. 9 10

MFR, IND, PUBLIC and PARK were not found to be endogenous. We perform two stage IV estimation using STATA and we use the command ivreg.

Y. Song, G.-J. Knaap / Regional Science and Urban Economics 34 (2004) 663–680

675

4.5. Model 5 with MIX_IV variables The results of Model 5 produced more interesting results. Results show that ratio of total jobs to residents (JOB) is insignificant. The positive sign of SERVJOB however shows that sale price is increasing if there is an increasing amount of service jobs in the neighborhood. Service jobs in this context are those population-serving jobs in sectors such as retail, personal services, entertainment, health, education, and other professional and related services.

5. Caveats It is important to note a few caveats before drawing conclusions. The first stems from the focus on single family sales. Because the assessor’s data did not identify the number or units in multifamily structures we were not able to include multifamily sales or thus analyze how mixed uses affect multifamily prices and rents. It is possible that multifamily residents have a quite different preference structure than single family residents. Second, our measures of land use mix capture only mixtures of uses across property lines. That is, because of the limitations of data, we were not able to explore the price effects of mixing uses on the same property, let alone the same building. Finally, our results apply only to Portland, OR, which, for a variety of reasons may not be representative of the United States, let alone the world.

6. Conclusions For half a century, American communities have employed zoning ordinances and other regulations that have resulted in segregated land uses, often reachable only by car. At the end of the 20th century and beginning of the 21st, many communities began experimenting with measures to encourage the mixing of uses that was once prevalent in cities and towns prior to its prohibition. Advocates of Smart Growth, New Urbanism, and other land use reforms suggest that mixing uses is a preferable development pattern to segregated uses, but little research has been done on the effect of mixed uses on housing prices. In this paper, we focused on Washington County, OR, where measures to encourage mixed use development are part of the required planning and development process. We first developed several quantitative measures of mixed land uses and computed these measures for various neighborhoods within Washington County. We then incorporated those measures in a hedonic price analysis. Through an instrumental variables approach, we also adjusted for the correlation between the endogenous neighborhood commercial land use variable and the error term. Our fundamental conclusion is that mixing certain types of land uses with single family residential housing has the effect of increasing residential property values. This is especially true for houses that are closer to public parks or are located in neighborhoods with a relatively large amount of land devoted to public parks. Housing prices also increase when they are close to neighborhood-scale commercial uses, or are part of a community with a relatively large amount of neighborhood-scale commercial uses. In

676

Y. Song, G.-J. Knaap / Regional Science and Urban Economics 34 (2004) 663–680

other words, a house tends to be sold at a higher price if it is closer to a public park or a neighborhood store. Additional premium exists when the neighborhood store is situated within pedestrian walkable distance. It is important to note that the research indicates that the size and scale of the commercial development is important to consumers. The larger or more intense the commercial development, the more it can have a negative effect on housing prices.11 The research also shows that housing prices are higher in communities that are dominated by single-family use and in which multi-family residential, commercial, industrial, public institutional and public park uses are evenly distributed. This suggests that, despite the premium associated with accessibility to parks and neighborhood commercial uses, consumers still value homogeneous residential neighborhoods. If that homogeneity is disturbed, however, it is better when the disturbance is a mix of nonresidential uses and not a single, nonconforming use. In addition, the research shows that housing prices are also higher in communities with relatively more service jobs. Service jobs in this context include retail, personal services, entertainment, health, education and other professional services. While the research generally shows that mixes of uses can boost nearby housing prices, it provides distinctly mixed support for other elements of smart growth. The research reveals, for example, that proximity to multi-family residential units can depress the prices of nearby single-family housing. This finding could be interpreted as bad news for advocates of higher density developments, which is a key element of smart growth strategies, as well as a troubling sign for advocates of low and moderate income housing. The research also revealed that single family homes were adversely affected by dwelling unit density, but not by population density. These somewhat contradictory findings have many possible explanations. A likely explanation, however, is that single family home buyers prefer to live in low density environments with large single family lots. Further, this preference seems to hold regardless of household size and regardless of the isolated highdensity apartment that might stand at the edge of the neighborhood. It is necessary to note, however, that we did not include any variables that capture the existence of visual barriers (such as berms, fencing, or trees) between single family homes and multi-family units. If multi-family units are to be located into neighborhoods with carefully designed measures to alleviate possible negative impacts due to congestion, unsightliness and density, their negative effects on single family property value might diminish. The same could be true for single family developments with small lots. We leave this issue for future exploration. In summary, property owners in many communities often perceive that mixed land uses will somehow diminish their property values and therefore often oppose the idea of bringing mixed land uses into their neighborhoods through rezoning or redevelopment. The results of this study, however, indicate that carefully designed mixed land use within neighborhoods can increase property values. Specifically, the research 11 We mentioned in footnote 2 that, in this study, we distinguish between neighborhood commercial and central/general commercial. We only include neighborhood commercial land uses in this study. We found discounts associated with being close to central/general commercial uses, or increasing amount of central/general commercial land uses within a neighborhood. For economy of exposition, however, we do not present the results here. Results are available from the authors.

Y. Song, G.-J. Knaap / Regional Science and Urban Economics 34 (2004) 663–680

677

shows that the following factors should be considered in designing mixed land use neighborhoods: 

The type of mixed land uses needs to be compatible with the surrounding single-family residences.  Public parks are always welcome.  New businesses in neighborhoods should be service-oriented.  Commercial developments should be appropriate to the neighborhood, scaled in size to fit the neighborhood, and should offer convenient access to pedestrians. Our results suggest, therefore, that mixing land uses is not smart enough. Really smart growth and developments involve careful selection in how land uses are mixed and assure that such mixing does not detract from the premium still associated with neighborhoods dominated by single family use.

Acknowledgements We thank Richard Arnott, Paul Cheshire, Stephen Sheppard and two anonymous referees for their helpful advice. We express our appreciation for the support from Lincoln Institute of Land Policy. We alone are responsible for any remaining errors.

Appendix A . Performance of control variables The results of our analysis on the control variables are provided in Table A1.

Table A1 Regression results of control variables Variable

Model 1 (MIX_I)

Model 2 (MIX_II)OLS

Model 3 (MIX_II)IV

Model 4 (MIX_III)

Model 5 (MIX_IV)

Intercept

10.252781* (0.51810) 0.000011* (0.00000) 0.000297* (0.00000) 0.007940* (0.00040) 0.000049* (0.00001) 0.003531*** (0.00754) 0.00146* (0.00032)

10.035960* (0.51848) 0.000011* (0.00000) 0.000298* (0.00000) 0.008010* (0.00040) 0.000048* (0.00001) 0.002470** (0.00754) 0.001310* (0.00032)

10.421900* (0.53657) 0.000011* (0.00000) 0.000298* (0.00000) 0.007940* (0.00040) 0.000050* (0.00001) 0.003830** (0.00766) 0.001160* (0.00033)

10.259890* (0.51847) 0.000011* (0.00000) 0.000298* (0.00000) 0.008050* (0.00040) 0.000049* (0.00001) 0.002480** (0.00750) 0.001210* (0.00032)

10.336150* (0.52165) 0.000011* (0.00000) 0.000296* (0.00000) 0.007960* (0.00041) 0.000049* (0.00001) 0.003464** (0.00768) 0.001460* (0.00032)

LOTSIZE FLOORSPACE AGE AGESQUARE INCITY SAT

(continued on next page)

678

Y. Song, G.-J. Knaap / Regional Science and Urban Economics 34 (2004) 663–680

Table A1 (continued) Variable

Model 1 (MIX_I)

Model 2 (MIX_II)OLS

Model 3 (MIX_II)IV

Model 4 (MIX_III)

Model 5 (MIX_IV)

HILLCBD

0.000000** (0.00000) 0.000007* (0.00000) 0.000008* (0.00000) 0.000412* (0.00000) 0.000009* (0.00001) 0.000005* (0.00858) 0.058950* (0.02236) 0.276320* (0.01033) 0.000019** (0.00511) 0.003800** (0.02681) 0.017500** (0.01202) 0.004310** (0.00220) 0.7488

0.000001** (0.00000) 0.000006* (0.00000) 0.000007* (0.00000) 0.000418* (0.00006) 0.000008* (0.00001) 0.000005* (0.00693) 0.061840* (0.01038) 0.273090* (0.02730) 0.000016** (0.00402) 0.003660** (0.00222) 0.017590*** 0.01463 0.002910** (0.00367) 0.7486

0.000000 (0.00000) 0.000007* (0.00000) 0.000008* (0.00000) 0.000407* (0.00006) 0.000008* (0.00001) 0.000005* (0.00622) 0.057004* (0.01000) 0.253330* (0.01070) 0.000018* (0.00508) 0.003670* (0.00232) 0.001238*** (0.01002) 0.004390** (0.00220) 0.7018

0.000001* (0.00000) 0.000007* (0.00000) 0.000007* (0.00000) 0.000427* (0.00006) 0.000009* (0.00001) 0.000005* (0.00693) 0.059730* (0.01033) 0.277880* (0.02689) 0.000011* (0.00402) 0.003110* (0.00220) 0.014210** (0.01357) 0.010350** (0.00346) 0.7494

0.000001** (0.00000) 0.000006* (0.00000) 0.000007* (0.00000) 0.000427* (0.00006) 0.000008* (0.00001) 0.000005* (0.007345) 0.063270* (0.01061) 0.292150* (0.02767) 0.000033** (0.00302) 0.003390** (0.00222) 0.016320** (0.01307) 0.01156*** (0.00969) 0.7498

BEAVCBD PORTCBD MINRDDIS MAJRDDIS GOLF MOUNTNVW INTCONN EXTCONN SFRDNSTY PEDCOM PEDBUS R SQUARE

*, **, and *** indicate significance level at the .001, .005 and .05 levels, separately. Standard errors are shown in parenthesis.

A.1 . Property physical housing attributes As expected, LOTSIZE and FLOORSPACE are positively related to house price. The expected negative coefficient on AGE reveals that an older home is worth less than a newer home, and the positive coefficient of AGESQUARE indicates that the relationship between house value and house age is not linear. A.2 . Public service levels Public services are capitalized into property values. As expected, INCITY is positively related to house price, reflecting the value of services provided by cities. The positive coefficient of SAT indicates that, school districts with higher SAT scores are associated with higher value of sales. A.3 . Location Location matters. Negative coefficients for the variables HILLCBD and PORTCBD, indicate that housing prices fall with distance from the Portland and Hillsboro CBDs.

Y. Song, G.-J. Knaap / Regional Science and Urban Economics 34 (2004) 663–680

679

Since the CBDs of Portland and Hillsboro are major employment centers, this was expected. The coefficient on the variable BEAVCBD, however, is positive. Though not expected, this likely reflects the character of downtown Beaverton as more of an automobile-oriented retail center than an employment center. Positive coefficient on MAJRDDIS indicates that housing price increases with distance from major roads. A.4 . Amenity and disamenities Amenities and disamenities affect housing prices as expected. The positive coefficient of GOLF is consistent with earlier studies. The positive coefficient on the binary variable MOUTNVW indicates that a view of the mountains increases property value. The coefficient of MINRDDIS has the expected negative sign, indicating that home buyers pay a premium for the houses with better accessibility to minor roads. The binary variable for exposure to traffic characteristics exhibits the expected relationships. The negative coefficient of ONMAJRD indicates that home buyers pay less for the houses that are within 150 feet of a major road for possible noise nuisance effects. A.5 . Socio-economic characteristics Socio-economic variables PCTWHITE and MEDINC are not significant, perhaps due to the lack of variation of race and income in our study area. A.6 . Street design and circulation systems The positive coefficient of INTCONN and ECTCONN indicate that home buyers pay a premium for an internally connective, but externally less connective neighborhood. A.7 . Density The negative coefficients of SFRDNSTY is consistent with the previous market surveys which reveal that houses in neighborhoods with low dwelling-unit density are sold at higher prices. POPDNSTY is not significant. A.8 . Pedestrian walkability For variables measuring pedestrian walkability to commercial uses and bus stops, the results are mixed. The positive coefficient of PEDCOM indicates that consumers pay a premium for more pedestrian accessibility to commercial uses, while the negative coefficient of PEDBUS indicates that homebuyers pay less for houses being too close to bus stops.

References Anselin, L., 1988. Spatial Econometrics: Methods and Models. Studies in Operational Regional Science Series. Kluwer Academic Publishing, Norwell, MA.

680

Y. Song, G.-J. Knaap / Regional Science and Urban Economics 34 (2004) 663–680

American Planning Association, 1998. The principles of smart development. PAS report #479, APA, Chicago, IL. Calthorpe, P., 2000. New Urbanism: Not perfect, but improving. Available online: http://www.pbs.org/newshour/ newurbanism/calthorpe.html. Cao, T.V., Cory, D.C., 1981. Mixed land uses, land-use externalities, and residential property values: a reevaluation. Annals of Regional Science 16, 1 – 24. City Council of Beaverton, 1999. Comprehensive plan for the City of Beaverton. Ordinance No. 4064. Available online: http://www.ci.beaverton.or.us/departments/CDD/ComprehensivePlan/maincomp.htm. City Council of Hillsboro, 2000a. Hillsboro comprehensive plan. Ordinance No. 2793-4-77. Available online: http://www.ci.hillsboro.or.us/Planning_Department/COMP_PLAN/COMPPLANTitle.pdf. City Council of Hillsboro, 2000b. Hillsboro subdivision ordinance. Ordinance No. 2808. Available online: http:// www.ci.hillsboro.or.us/Planning_Department/SUB_ORD/Sub_Ord_Title.pdf. Congress of New Urbanism, 2002. Available online: www.cnu.org. Eppli, M.J., Tu, C.C., 1999. Valuing the New Urbanism: The Impact of the New Urbanism on Prices of SingleFamily Homes. ULI—the Urban Land Institute, Washington, DC. Geoghegan, J., Wainger, L.A., Bockstael, N.E., 1997. Spatial landscape indices in a hedonic framework: an ecological economics analysis using GIS. Ecological Economics 23, 251 – 264. Irwin, E.G., 2002. The effects of open space on residential property values. Land Economics 78 (4), 465 – 480. Irwin, E.G., Bockstael, N.E., 2001. The problem of identifying land use spillovers: measuring the effects of open space on residential property values. American Journal of Agricultural Economics 83 (3), 698 – 704. Katz, P., 1994. The New Urbanism: Toward an Architecture of Community. McGraw-Hill, New York. Li, M.M., Brown, H.J., 1980. Micro-neighborhood externalities and hedonic housing prices. Land Economics 56 (2), 125 – 141. Grether, D.M., Mieszkowski, P., 1980. The effects of nonresidential land uses on the prices of adjacent housing: some estimates of proximity effects. Journal of Urban Economics 8 (1), 1 – 15. Metro, 1992. The Metro 2040 growth concept. Available online: http://www.metro-region.org/growth/tfplan/ gconsum.html. Metro, 2001. 2040 Regional framework plan and functional plan. Available online: http://www.metro-region.org/ growth/tfplan/framplan.html. Metro, 2002. Livable communities workbook. Available online: http://www.metro-region.org/article.cfm? articleid=436. Smart Communities Network, 2002. Available online: http://www.sustainable.doe.gov/. Song, Y., Knaap, G.J., 2003. New urbanism and housing values: a disaggregate assessment. Journal of Urban Economics 54, 218 – 238. Song, Y., Knaap, G.J., 2004. Measuring urban form: is Portland winning the war on sprawl? Journal of American Planning Association 70 (2), 210 – 225. Turner, M., Gardner, R.H., O’Neill, R.V., 2001. Landscape Ecology in Theory and Practice: Pattern and Process. Springer Verlag, New York. Victoria Transport Policy Institute, 2003. Smart growth policy reforms: changing planning, regulatory and fiscal practices to support more efficient land use. Available online: http://www.vtpi.org/tdm/tdm95.htm#_ Toc31066912.