Essays on applied spatial econometrics and housing economics

Housing purchase represents one of a household's most significant economic decisions. The ancient joke in real estate is that the three most important criteria for selecting a house are location, location, and location. This explains the great emphasis of a household on residential location choice when he/she is buying a home. Driven by households' demand on location, it should also play an important role in determining house prices. As a key determinant in household consumption behavior, locational context or neighborhood effects is worth investigation. This dissertation examines locational/neighborhood effects in the housing market using spatial econometric methods.; The first essay studies the importance of social interactions in a household's location decision. I argue that individuals prefer interacting with others who have similar socioeconomic backgrounds. The hypothesis that a household desires to find a good community match is tested through the application of a discrete residential location choice model. An unwritten rule in real estate is that one should buy the cheapest house in an expensive neighborhood, which is formally the Tiebout hypothesis that households search for a community where their benefits from local public goods will exceed their local tax costs. The community matching hypothesis and the Tiebout hypothesis have different implications regarding a household's residential location choice. Community matching implies households will prefer similarity, while the Tiebout hypothesis implies households will prefer neighborhoods with richer neighbors. I use a nested logit (NL) regression to analyze a household's residential community decision within Franklin County, OH. As an important input of the NL regression, a set of housing price indices are created through a spatial error model. The regression results support the hypothesis that a household prefers neighbors with like socioeconomic characteristics in almost all of the similarity dimensions and only prefers an affluent neighborhood to a moderate degree.; The second essay employs a spatial autoregressive model (SAR) to estimate housing asset prices. Applying the rational expectations hypothesis, this essay models the current value of a housing unit as the conditional expectation of the discounted stream of housing services accruing to the owner of the house. Based on the importance of location, the value of housing services is determined by neighborhood effects as well as the physical attributes of the property itself. In the existing hedonic literature, the neighborhood effects are only ascribed to prior transactions in the neighborhood. After employing the generalized method of moments (GMM) in estimating the spatial asset pricing model, I find that both expected future transactions and prior transactions in the neighborhood are significant in explaining a house's price, and the explanation power of future neighborhood transactions is statistically equivalent to that of past neighborhood transactions. The inclusion of expected future transaction prices in the neighborhood takes into account the influence of expected changes in the community and factors these potential changes into the house prices. This is consistent with the forward-looking behavior of households. The forward-looking model generates superior out-of-sample prediction performance relative to the conventional hedonic model.