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Satisfaction with neighbors and neighborhood housing prices John P. Blair and James E. Larsen Wright State University, Dayton, Ohio, USA
194 Abstract
Purpose – The purpose of this study is to test the hypothesis that neighborhoods characterized by satisfying social relationships among residents have higher housing prices than areas where people are less satisfied with their neighbors. Design/methodology/approach – A semi-logarithm regression model is used to test whether the extent of satisfaction with neighbors is significantly related to transaction prices of houses in 59 neighborhoods in Dayton, Ohio. Findings – The results are consistent with, and more specific than, previous studies linking social capital to neighborhood stabilization. Resident satisfaction with their neighbors is found to be an important determinant of property value controlling for housing characteristics. Practical implications – The findings support stabilization and economic development strategies that seek to enhance social relationships in urban neighborhoods. Originality/value – This study is the first effort to examine the impact of relations among neighbors on housing prices while controlling for traditional housing characteristics. The paper is an important step in unbundling the social capital concept and points policy makers in directions that can improve community property values. Keywords Housing, Social capital, Regression analysis, Residential areas, United States of America Paper type Research paper
Journal of Place Management and Development Vol. 3 No. 3, 2010 pp. 194-204 q Emerald Group Publishing Limited 1753-8335 DOI 10.1108/17538331011083934
1. Introduction Satisfying social relationships are an important part of life quality and are often localized in neighborhoods. The link between social relationships and neighborhood desirability is at once obvious and complex. On one hand, it is obvious that individual isolation and social pathologies render neighborhoods undesirable. Conversely, friendly neighborhoods are considered a selling point. On the other hand, social relationships are complex, serve numerous purposes and reflect manifold contextual subtleties. Consequently, ambiguities about which types of social relationships most affect housing markets can result. If social relationships provide significant, positive value to residents, housing market outcomes are likely to be influenced as individuals’ attempt to maintain or capture these benefits. Satisfaction with neighbors is an intuitive and understandable component of social capital. It may be helpful in understanding housing markets even without the other identifiers of social capital. The hypothesis of this study, therefore, is that satisfaction with neighbors is an important element of social relationships and is reflected in house prices. In the present paper, a model to empirically investigate the significance of satisfaction with neighbors on residential property values is developed and tested. To our knowledge, this is the first study to examine the impact of relations among neighbors on housing prices while controlling for other housing characteristics. The results indicate that, ceteris paribus, satisfaction with neighbors and housing prices
are positively related. This finding has important implications for place managers and other persons interested in improving neighborhoods. The remainder of the paper is organized as follows. Section 2 contains a brief review of the literature on social capital as it relates to housing markets. The model and methodology are described in Section 3 and the results are presented in Section 4. Conclusions and implications for neighborhood stabilization policy are discussed in Section 5. 2. Social relationships and neighborhood outcomes Discussion of social capital represents the most recent theoretical effort to link social relationships and economic processes. Putnam (2000) defined social capital as the ties that connect networks of individuals to one another. These relationships are strengthened by shared norms, especially trustworthiness and reciprocity. Numerous other analysts have embraced the concept and offered alternative definitions. The proliferation of attitudes, behaviors and institutions that reflect social capital has created both conceptual and measurement ambiguities. The lack of clarity has contributed to the resistance of some prominent economists towards social capital concepts (Arrow, 2000; Solow, 2000). There is currently no precise definition of social capital upon which scholars agree. As Mayer (2003, p. 110) summarized, “Though the exact meaning of the social capital concept has been transformed and become more differentiated in the course of this career, it has not gained in precision.” In spite of ambiguities, the concept of social capital refreshed the insight that strong social relationships can enhance neighborhood conditions and improve local housing markets. Colman (1990) saw social capital principally as a resource for achieving ends that would be more difficult to attain in its absence. Neighborhood improvement is widely believed to be one such end. A complete review of studies describing the nexus between social capital and neighborhood improvements is beyond the scope of this paper. However, four categories of benefits from living in neighborhoods with strong social networks can be identified (Gittell and Vidal, 1998). First, there is an intrinsic value in living in a friendly environment (Williamson et al., 2002). Second, there are numerous studies documenting the salutary effects of such environments on aspects of health (Carlson and Chamberlain, 2003; Almedon, 2005). Third, many important economic benefits have been identified including enhancing job searches (Fernandez and Castilla, 2001), expediting business financing, encouraging innovation and entrepreneurship (Trigila, 2001) and supporting social and informal economies (Lukkarinen, 2005). Fourth, important quality-of-life factors including amelioration of crime (Choe and Peterson, 2009), improved quality of life for the elderly (Nilsson et al., 2006), school performance (Woolley et al., 2008) and general wellbeing have been shown to be improved through the presence of social capital. Taken as a whole, the empirical literature showing beneficial effects of social capital on neighborhood welfare is substantial. Housing market analysts recognize that neighborhood conditions affect housing markets and, accordingly, have incorporated social capital into models of housing market dynamics. A link between housing tenure (rent or own) and the presence of social capital has been identified. Brisson and Usher (2007) showed that individuals with “informal neighborhood bonding social capital” have higher rates of home ownership and probably less transience among poverty-level families. The effect of social capital on the decision to relocate was tested
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by Kleinhans (2009). He found no significant relationship between access to social capital and the propensity of residents to move from the neighborhood, a finding at odds with expectations. Another way to examine the impact of social capital on housing markets is to consider house prices. In a pioneering study, Temkin and Rohe (1998, p. 70) hypothesized that social capital would allow communities to mobilize and form alliances outside the neighborhood, thus influencing decisions that “affect the neighborhood character over time.” Accordingly, they reasoned that the presence of social capital will impede “downward succession.” Statistical support for the hypothesis was provided by the positive correlation between a host of social capital indicators and the ten-year change in average home values in Pittsburgh census tracts. Unfortunately, since census tracts as a whole were analyzed, characteristics of individual houses sold could not be incorporated in the study. An important criticism of the empirical literature linking social capital to housing markets follows from the theoretical ambiguity of the social capital concept. Typically, models are constructed and social capital is hypothesized to be an independent variable. An index is constructed to proxy social capital. Sometimes, the variables that reflect social capital are based on surveys specifically designed to measure social capital, but often they are taken from datasets designed for another purpose. Sometimes, the variables representing social capital are selected based on best fit rather than theoretical reasoning. When the many components that constitute social capital are aggregated in an index, the impact of some important components may be diluted. For instance, to judge the effect of social capital on an individual’s likelihood to move, Kleinhans (2009) used (among other things) the extent of agreement with two statements: “in this neighborhood we are on good terms with each other” and “in this neighborhood there is a good level of social control.” Each statement is an appropriate indicator of social capital, but they may have contrary or dissimilar affects on housing markets. While it is reasonable to assume that being on good terms with neighbors would motivate local residents to remain in the area, some manifestations of social capital, such as high levels of social control, may be off putting and have a contradictory impact on housing markets compared to other measures in the index. The need for more clarity regarding specific aspects of social capital’s influence on housing markets is particularly pressing among place managers seeking to improve neighborhoods. For example, the number of voluntary organizations in an area has been used to indicate the presence of social capital. However, it is possible that such indicators can be generated in “mechanical” ways so that the important social networks characterized by trusting and reciprocating relationships remain lacking. If this is the case, indicators of social capital could be created without the substance or enhanced social networks researchers have associated with social capital. Previous research suggests that satisfaction with neighbors may be a critical component of social capital. Podobnik (2002) argued that personal friendships are important elements of social capital. He found community friendliness to be the most highly valued neighborhood feature examined and it was strongly associated with participation in other groups in a case study of Portland, Oregon. Licamele and Getoor (2006) showed friendships to be instrumental ingredients in social capital networks designed to advance academic research. Blair and Carroll (2009) observed that social
capital often evolves from nano exchanges such as a smile, nod or pleasant word. In short, personal relationships appear to be building blocks to more developed manifestations of social capital such as formal associations, civic organizations and so forth. The present study extends this stream of research by investigating whether the simple metric, satisfaction with neighbors, is reflected in housing prices.
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197 3. The model and methodology The theoretical underpinning supporting our model is straightforward. Market mechanisms will transmit the relationship between neighborhood satisfaction and house prices. On the supply side, residents who are highly satisfied with their neighbors will be less willing to offer their homes for sale and/or less likely to accept low offers. The opposite attitude will characterize residents who are dissatisfied with their neighbors. On the demand side of the market, the price effect depends upon the perception of potential buyers regarding the level of satisfaction held by current neighborhood residents. Buyers who view an area as a place where they believe they can form satisfying relationships with their neighbors will be more likely to search for a home in that area and/or pay higher prices. The opposite demand side effects will characterize neighborhoods where buyers perceive low levels of neighbor satisfaction. Hence, housing prices will be higher, the greater satisfaction with neighbors. In essence, people will vote with their feet and their wallets in favor of neighborhoods that promise satisfying neighbor relations. The present study differs from previous research in at least two important ways. First, it looks at actual housing transactions to measure price effects rather than considering overall average price effects or propensity to move from a census tract. This approach allows us to control for differences in property characteristics such as structure age, number of rooms, lot size and so forth. Second, rather than aggregating a variety of proxy variables for social capital, this paper employs a narrower, more concrete, and intuitively understandable measure of the social relationships among neighbors – satisfaction. Data analyzed in the present study were obtained from two sources. First, the level of satisfaction with neighbors was derived from a comprehensive public opinion survey sponsored by the City of Dayton and administered via telephone to 1,435 city residents. The survey covered a multitude of topics of interest to local policy makers (and it can be viewed at: www.cityofdayton.org/departments/omb/Documents/POS07.pdf). For the purpose of the present study, we focus on the survey question, “How satisfied are you with your neighbors?” Survey participants were limited to one of four responses ranging from very satisfied to very unsatisfied. For the purpose of this study, we assigned NSAT (our neighborhood satisfaction variable) a value of 4, 3, 2 or 1, if the respondent was very satisfied, somewhat satisfied, somewhat dissatisfied or very dissatisfied with their neighbors, respectively. Unlike the other determinants of housing prices in the model, NSAT is not associated with an individual house but reflects the average satisfaction for the respondents in the subject neighborhood. This approach makes theoretical sense because prices are the outcome of relatively competitive markets. The price of a particular property in a given neighborhood will not necessarily vary depending upon whether a particular seller is satisfied with his or her own neighbors. The advantage of “likeable” neighbors should accrue to all homes for sale regardless of the disposition
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Table I. Statistical summary of selling price, explanatory and satisfaction variables
of an individual seller just as a low level of crime should accrue to all houses in a neighborhood regardless of whether a particular home was robbed. The Dayton City Planning Department defines a neighborhood as a well-defined area where unique physical and social characteristics are in harmony. The neighborhood area must be large enough to sustain an array of distinctive public services and strong social structures while being small enough to provide a sense of place, orientation and control. The ideal population of a neighborhood in Dayton is approximately 3,000-5,000 residents. Major physical barriers (e.g. rivers, elevated freeways, major thoroughfares or a major non-residential zone) which limit access and social integration between residential areas serve as neighborhood boundaries for many Dayton neighborhoods. In other cases, boundaries were established where there was an abrupt change in the type, style, or age of the housing stock, or a change in street pattern. Since 1991, Dayton has consisted of 65 neighborhoods. Six neighborhoods were excluded from this study because there were not enough observations from these neighborhoods to provide statistically reliable results. Examination of NSAT in Table I will reveal that (with a mean value of 3.12) survey participants, in aggregate, were slightly more than “somewhat satisfied.” There was, however, a considerable amount of variation in neighborhood satisfaction between residents of the 59 neighborhoods in our study. Two important features follow from drawing the sample from a single tax jurisdiction, Dayton, Ohio. First, previous literature has stressed that social capital is very valuable in low- and moderate-income areas. Because Dayton is a central city that has suffered substantial distress during the deindustrialization decades, almost all of the areas in our sample are either low or moderate income. Second, the fact new construction is very low throughout most city neighborhoods means the housing supply has been relatively unaffected by new construction. The predicted price effect of individuals selecting neighborhoods based on satisfaction with neighbors may be obscured if the supply of housing is elastic. In this case, increases in demand may be absorbed by new Variable
Mean
SD
Minimum
Maximum
NSAT PRICE SQFT LOT AGE FRAME BRICK REHAB AIR NONE FULL A-COND B-COND D-COND E-COND OWN SPRING FALL WINTER
3.12 44,134.61 1,297.85 5,766.97 79.72 0.736 0.139 0.025 0.242 0.131 0.703 0.003 0.020 0.261 0.002 0.724 0.273 0.231 0.223
0.186 37,351.53 446.64 4,667.66 21.15 0.440 0.346 0.157 0.428 0.337 0.457 0.051 0.139 0.439 0.045 0.447 0.445 0.421 0.417
2.67 3,000 700 1,089 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
3.83 401,806 6,712 208,696 205 1 1 1 1 1 1 1 1 1 1 1 1 1 1
construction rather than price increases. If construction costs remain constant, new houses will be supplied at about the cost of previous units. Hence, the model will best be tested in a developed environment rather than a “green fields” setting. Property prices and detailed information about characteristics of specific properties for all single-family house transactions that occurred in the City of Dayton during 2007 were obtained from the Montgomery County, Ohio Assessor’s Office. From the total of 3,780 single-family house transactions supplied, 350 were eliminated due to missing (or obviously incorrect) property characteristic information leaving 3,430 observations in our sample. Table I shows summary statistics for the properties in the sample. The mean transaction price (PRICE) in our sample was $44,135, reflecting low incomes in the central city and the housing deterioration in some areas. Substantial previous research suggests that the estimated coefficient for the number of square feet (SQFT) in the house and for the number of square feet in the building lot (LOT) should be positive and the estimate for house age (AGE) should be negative. REHAB was included to control for the possibility that a significant rehabilitation of the house may reduce the effective age of the structure. REHAB was assigned the value 1 if according to the Assessor’s Office the property had been rehabilitated, or 0 if not. The expected sign for REHAB is positive. The database also contained 13 additional binary variables. Previous research suggests that houses with a brick exterior should sell at a premium, and it is intuitive that houses in our sample with wood exteriors may sell for discounts compared to the holdout category – aluminum/vinyl clad houses. Therefore, the expected sign of the estimate is positive for BRICK and negative for FRAME. The expected sign for central air conditioning (AIR) is positive. A priori, compared to the holdout category, houses with a partial basement, the effect of the presence of a full basement (FULL) or no basement (NONE) is indeterminate. Basements in Dayton not only offer protection during storms but also have a tendency to flood. Four property condition levels were included in the model. Compared to the holdout category, properties in average condition, the expected sign is positive for properties in above average condition (A-COND and B-COND) and negative for properties in below average condition (D-COND and E-COND). OWN was included to control for the possibility that investors pay less for property than purchasers who intend to reside in the house. If the County Auditor’s Office listed a property as subject to a 2.4 per cent reduction in property tax applicable only to owner-occupied dwellings, OWN was assigned a value of 1, otherwise 0. A positive coefficient is anticipated for OWN. Previous research, including a study by Wolverton and Bottermiller (2003), suggests that transaction prices for houses sold in the Summer (June, July and August) may sell for more than comparable houses sold during other months. Therefore, the expected sign for properties sold in the Spring (March, April and May), Fall (September, October and November) or Winter (December, January and February) is negative. The following model was estimated using the SAS (2004) PROC REG procedure: 18 X bj PC þ 1 ð1Þ SP ¼ a þ b1 NSAT þ j¼2
where: SP ¼ the transaction price. a ¼ the intercept.
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b ¼ the estimators. NSAT ¼ mean neighborhood response to survey question “how satisfied are you with your neighbors?”. PC ¼ a vector of 17 (previously described) property characteristic control variables. 1 ¼ the error term. The functional form utilized to estimate equation (1) is semi-logarithmic. Specifically, we employed the extended Box and Cox (1964) regression, where both the dependent and all nonbinary-independent variables are transformed by the same power transformation. Rosen (1974), Lucas (1975) and Linneman (1980) and others assert that a linear functional form is theoretically inappropriate because the regression is an approximation of a multifaceted (nonlinear) consumption possibilities frontier. In the present case, a nonlinear functional form is critical in order to correctly interpret the variable of interest, NSAT. With a linear dependent variable, the size of the estimated coefficient will be affected by the values used to construct an index such as NSAT. If, for example, the values are doubled, the value of the estimate for this variable will decrease by 50 per cent. The estimates in the extended Box-Cox regression, on the other hand, are unaffected by the scale of the index. The Goldfeld and Quandt (1965) test was conducted on several suspect independent variables to determine if the error terms of the model were homoskedastic. The null hypothesis of homoskedasticity could not be rejected for the natural logarithm of SQFT. The critical F-test statistic, with a ¼ 0.05 and 120 and 120 degrees of freedom, is 1.352; the test statistic was 1.213. The null hypothesis of homoskedasticity was rejected, however, for both the natural logarithm of LOT and the natural logarithm of AGE. The test statistics were 1.371 and 1.783. Therefore, the final estimate of equation (1) was conducted using generalized least squares regression where the dependent variable and all independent variables are adjusted by the inverse of the natural logarithm of AGE, squared (nearly identical results, not detailed here, were obtained adjusting equation (1) by the natural logarithm of LOT). In our final estimate of equation (1), the COLLIN option was specified in the PROC REG procedure. The results do not indicate a serious degree of multicollinearity. The estimation results are summarized in Table II. 4. Results Examination of Table II, where the estimation results of equation (1) are summarized, reveals that the data fit the model fairly well. The F-value of 919.62 is highly significant, and the adjusted R 2 indicates that the independent variables explain 82 per cent of the variation of the dependent variable. We do not provide a detailed interpretation of the results for each of the property characteristic variables here as they were included solely to facilitate our analysis of NSAT by controlling for price differences attributable to these variables. It is worth noting, however, that nine of the control variables are significant at the 99 per cent confidence level and one is significant at the 90 per cent level. The estimates for all of the significant control variables have signs that are consistent with theoretical expectations and previous studies. For six control variables, however, there was no statistically significant difference between the estimated coefficient and 0.
Variable
Parameter estimate
SE
t-value
Pr . jtj
NSAT AGE SQFT LOT FRAME BRICK REHAB AIR NONE FULL A-COND B-COND D-COND E-COND OWN SPRING FALL WINTER
0.77631 20.25706 0.52037 0.37445 0.04968 0.19280 0.30887 0.22153 0.02036 0.06853 20.10057 0.05479 20.21739 0.20503 0.21456 0.00058 20.12428 0.04744
0.20961 0.02831 0.03852 0.03385 0.03927 0.04840 0.08250 0.03159 0.05095 0.03710 0.23765 0.08604 0.03050 0.29117 0.02935 0.03453 0.03564 0.03637
18.02 2 9.08 13.51 11.06 1.27 3.98 3.74 7.01 0.40 1.85 2 0.42 0.64 2 7.13 0.70 7.31 0.02 2 3.49 1.30
, 0.0001 , 0.0001 , 0.0001 , 0.0001 0.2059 , 0.0001 0.0002 , 0.0001 0.6895 0.0648 0.6722 0.5243 , 0.0001 0.4814 , 0.0001 0.9866 0.0005 0.1922
Notes: n ¼ 3,430; F-value ¼ 919.61; Pr . F ¼ ,0.0001; R 2 ¼ 0.8208; Adjusted R 2 ¼ 0.8200
With a Box-Cox regression, the estimated coefficients are elasticities instead of dollar values, and for binary variables, a significant coefficient indicates the percentage change in selling price attributable to the presence of the explanatory variable. The results for FULL, for example, suggest that buyers in Dayton valued properties with a full basement. The estimated coefficient indicates that houses in our sample with a full basement sold, on average, for approximately 6.9 per cent more than houses with a partial basement. For continuous variables, the elasticities can be used to calculate the expected percentage change in selling price for a given percentage change in the independent variable. For example, the coefficient for SQFT is 0.52037. This indicates that if one house is larger than another by 10 per cent (e.g. 1,650 square feet compared to 1,500 square feet), the larger house should sell for 5.2037 per cent more than the smaller house, ceteris paribus. Focusing on the variable of primary interest, the estimated coefficient for NSAT was positive and highly significant, indicating that neighborhoods with high (low) neighbor satisfaction have higher (lower) housing prices. The large parameter estimate of 3.77631 suggests practical as well as statistical significance. It indicates that in our sample a 10 per cent difference in NSAT, for example, was associated with a 37.7631 per cent difference in selling price. To avoid overstating the practical importance of NSAT, however, it should be recognized that a 10 per cent change is very large compared to the standard error of 0.20961. An example will illustrate that a 10 per cent change in NSAT is a substantial change. Consider a hypothetical neighborhood where NSAT is 3.0 (somewhat satisfied). Assume this value obtained because 30 per cent of the neighborhood residents were very satisfied, 40 per cent were somewhat satisfied, and 30 per cent were somewhat unsatisfied. In this example, one way satisfaction would increase to 3.3 would be if everyone who was somewhat
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Table II. Regression results: dependent variable – housing price
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dissatisfied becomes somewhat satisfied. It is unlikely that this magnitude of change would occur in a short time frame. 5. Conclusions and implications Improving social relationships is an established tool for building neighborhoods as well as a desirable end in itself. This paper hypothesized that the benefits of satisfaction with neighbors will be capitalized into housing prices. The empirical analysis was consistent with the hypothesis. The model extended the use of housing price regression models to an important yet previously understudied area. The findings inform the larger discussion of social capital by suggesting that satisfaction with ones neighbors may be an important part of the mix of variables that are currently considered elements of social capital. While this paper demonstrates the relationship between satisfaction with neighbors and housing prices, the mechanisms by which price changes is not fully understood. Place managers may find it useful to know the extent to which higher housing prices are driven by supply side factors (fewer houses offered for sale or offered only at higher prices) compared to demand side factors (more potential buyers or buyers willing to pay higher prices). If future research indicates that increases in satisfaction with neighbors are accompanied by higher prices and fewer sales, observers could conclude that residents are choosing not to relocate and prices are being driven by supply side factors. Since knowledge about likeability of neighbors is more easily known by persons already in the community, this mechanism seems most probable. Alternatively, if price increases are accompanied by a greater number of sales, the price increases might be driven by greater demand. A greater number of potential residents may be seeking to buy into the area. The major implication of our findings is that place managers should strive to build stronger relationships among neighbors. These efforts may prove useful both in developing new neighborhoods and in stabilizing others. In the planning stage, private developers, working through architects and other physical planners, can have major impacts. Podobnik (2002) suggested that communities could be designed to encourage the types of neighbor interactions that first create good relations among neighbors and later create more complex forms of social capital. Such neighbor-friendly designs include walkable communities, integrated residential and commercial land uses, common green space, non-grid streets and front-porch-oriented housing. Designs to enhance neighbor relationships underlie the “new urbanist” movement in physical design. Although largely supported by case studies and other non-statistical methodologies, there is preliminary evidence that social capital is stronger in developments built on new urbanist principles. (In one study, a higher percentage of “lost” letters were returned unopened in new urbanist communities than in traditionally designed places: (Sander, 2002)). A definitive conclusion about the relationship between new urbanist physical design and the generation of social capital awaits more evidence. Changes in physical infrastructure are not always feasible, particularly in older, transitioning neighborhoods. Often, these areas have the least social capital and neighbors may be most isolated from each other. In these cases, public officials and community developers still have potential to improve satisfaction among neighbors and hence enhance housing prices. Support for community organizations and events may be a way to strengthen social capital without making significant infrastructure changes. Providing these “social director” functions has proven valuable in some retirement and resort communities. Corresponding activities such as block
parties or community clean-ups may also benefit neighborhoods where residents lack opportunities for satisfying dealings with their neighbors. Although developing opportunities for interaction are likely to be useful intrinsically, and also support property values, academics and place managers still lack a solid understanding about how to encourage better relationships among neighbors. The results of the present study suggest that further research towards this end should prove valuable.
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Mayer, M. (2003), “The onward sweep of social capital: causes and consequences for understanding cities, communities and urban movements”, International Journal of Urban and Regional Research, Vol. 27 No. 1, pp. 110-32. Nilsson, J., Rama, A.K. and Kabir, Z.N. (2006), “Social capital and the quality of life in old age”, Journal of Aging Health, Vol. 18 No. 3, pp. 419-34. Podobnik, B. (2002), “New urbanism and the generation of social capital: evidence from Orenco Station”, National Civic Review, Vol. 91 No. 3, pp. 245-55. Putnam, R.D. (2000), Bowling Alone: The Collapse and Revival of American Community, Simon and Schuster, New York, NY. Rosen, S. (1974), “Hedonic prices and implicit markets: product differentiation in pure competition”, Journal of Political Economy, Vol. 82 No. 1, pp. 34-55. Sander, T.H. (2002), “Social capital and new urbanism: leading a civic horse to water?”, National Civic Review, Vol. 91 No. 3, pp. 213-34. SAS (2004), SAS OnlineDocw 9.1.3., SAS Institute, Cary, NC. Solow, R.M. (2000), “Notes on social capital and economic performance”, in Dasgupta, P. and Serageldin, I. (Eds), Social Capital: A Multifaceted Perspective, The World Bank, Washington, DC. Temkin, K. and Rohe, W.M. (1998), “Social capital and neighborhood stability: an empirical investigation”, Housing Policy Debate, Vol. 9 No. 1, pp. 61-88. Trigila, C. (2001), “Social capital and local development”, European Journal of Social Theory, Vol. 4 No. 4, pp. 437-42. Williamson, T., Imbroscio, D. and Alperovitz, G. (2002), Making a Place for Community, Routledge, New York, NY. Wolverton, M.L. and Bottermiller, S.C. (2003), “Further analysis of transmission line impact on residential property values”, Appraisal Journal, Vol. 71 No. 3, pp. 244-52. Woolley, M.E., Grogan-Kaylor, A., Gilster, M., Karby, R.A., Grant, L.M., Reischl, T.N. and Alaimo, K. (2008), “Neighborhood social capital, poor physical conditions, and school achievement”, Children and Schools, Vol. 30 No. 3, pp. 133-45. About the authors John P. Blair received his PhD from West Virginia University. He is a Distinguished Research Professor of Economics at Wright State University. Prior to joining Wright State, he served as a Policy Analyst in the Department of Housing and Urban Development and was in the Urban Studies Department at the University of Wisconsin – Milwaukee. He has received the Roapke Award for research in economic development, The Distinguish Alumni Award from West Virginia University, Regional Research Institute and the Bloomberg Award for excellence in futures studies. John P. Blair is the corresponding author and can be contacted at:
[email protected] James E. Larsen received his PhD from The University of Nebraska. He is a Professor of Finance at Wright State University. Prior to joining Wright State, James E. Larsen served as the Faculty at Marquette University, University of Nebraska and Creighton University.
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