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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, neighborhood effects are worth investigation. This dissertation examines 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. This hypothesis suggests that a household desires to find a good community match. 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 fiscal surplus. 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 decision within Franklin County, OH. The 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. The estimation results reveal 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.