An analysis of the neighborhood impacts of a mortgage assistance program: A spatial hedonic model

Abstract

Abstract

Empirical Innovations in Policy Analysis

“…We may not think of public policy as “spatial” but in reality virtually all policy interventions are affected both by specific place‐related contextual factors as well as factors related to neighboring places. Thus, we observe similar analyses related to land‐use regulation and rental housing (Schuetz, ), job creation and business enterprise zones (Kolko & Neumark, ), traffic congestion pricing (Hårsman & Quigley, ), mapping jobs to urban ethnic enclaves (Liu, ), spatial regression of mortgage assistance program effects (Di, Ma, & Murdoch, ), and hazardous waste enforcement in low‐income and higher minority locations (Konisky, ).…”

Broadening Benefits from Natural Resource Extraction: Housing Values and Taxation of Natural Gas Wells as Property

“…The variables employed include demographic variables (share of the population that is white, the share in three different age groups, the share with some college education), income variables (the share of the population in three different household income categories, median household income), urbanization variables (the percent of the zip code in an urban area, population density), and real estate variables (the percent of vacant housing units, the median year built, median real estate taxes). These are common control variables in housing value studies: Schwartz and Zorn () (population density and age demographics); Santiago, Galster, and Tatian () (real estate taxes and income); Ellen and Voicu (2006) (proximity indicators); Di, Ma, and Murdoch () (racial demographics); Bifulco () (age and racial demographics). Clearly, other factors affect housing values, but as previously mentioned, identification of the oil and gas tax base capitalization comes from spatial variation in geology and temporal variation in natural gas prices, both of which should be unrelated to local housing market shocks (excluding those related to shale development).…”

Impact of Heavy-rail-based Rapid Transit on House Prices: Evidence from the Fremont, CA, Warm Springs BART Extension Project

“…I checked for spatial dependence by first creating a spatial weights matrix, W. I weighted the sale price (p) by considering both the spatial and temporal proximity of the sale transactions. Using the methodology employed by Di, Ma, and Murdoch (2010), I included the four nearest sale transactions in the spatial weights calculation. The transactions were further weighted by the proximity of the sale year.…”