Association for Public Policy Analysis & Management

Housing inequality in urban neighborhoods has gathered considerable attention in various academic disciplines and in policy. The knowledge about one’s own housing values is deeply related to urban inequalities and housing inequality therein. A few studies have indicated that the knowledge about one’s housing value shows different levels of accuracy depending on the homeowner’s individual characteristics. Comparing owners’ perceived housing values to appraised/assessed values and to sales prices, they have also shown that homeowners tend to overestimate their housing prices in general. However, there is a dearth of research that systematically explores how the knowledge about one’s home values are interconnected with wider patterns of social inequalities, especially focusing on urban areas.

In our study, we address the gap in the literature and investigate spatial patterns and multilevel determinants of such misestimation. First, we explore the relationship between individual- and neighborhood-level sociodemographic characteristics and the discrepancy between perceived vs. actual housing values in Detroit. Furthermore, we examine the possibility of spatial clustering in the knowledge about residential property values that would exacerbate urban inequalities.

We link two different sources of data for this inquiry. First, to derive current estimates of housing market values, we use the parcel-level data about sales prices and dates of homes accumulated by the Corelogic corporation. In cases where such information is absent, we estimate the market value from market values of houses within the same neighborhood, adjusting for housing attributes such as the size (area and numbers of rooms) and built year. Second, we use survey data from the Detroit Metropolitan Area Communities Study (DMACS), in which 203 homeowners report their estimates of housing values in 2017. Comparing data from these sources, we derive the level of discrepancy between perceived vs. actual housing value for each home.

We use two complementary empirical methods, one testing for spatial patterns of such discrepancy, and the other for investigating factors affecting the discrepancy. First, we map the level of discrepancy by individual characteristics of homeowners (level of education, income, and race) and by neighborhood characteristics (socioeconomic characteristics and racial composition); we also conduct hot spot analysis to identify clusters within Detroit where the discrepancy is significantly higher or lower than the expected values. Second, we utilize a logistic regression approach to explain the determinants of the discrepancy at individual- and neighborhood-levels.

By investigating the factors that influence the knowledge of housing values, this study would contribute to housing and urban inequalities research. Furthermore, our focus on the case of Detroit metropolitan area would shed light on urban policy, particularly in areas where the housing market has undergone rapid changes and in historically segregated neighborhoods.