Spatial Econometric Analysis of Dana Point's Housing Market

TL;DR

This study uses spatial econometric models and instrumental variables to analyze the factors influencing Dana Point's housing prices, accounting for temporal trends, spatial effects, and endogeneity issues.

Contribution

It introduces a comprehensive spatial econometric approach with robust instrumentation to better understand housing price determinants in Dana Point.

Findings

Spatial effects significantly influence housing prices.

Instrumental variable approach confirms the endogeneity of square footage.

Different spatial models reveal varied price dynamics across clusters.

Abstract

This paper investigates the determinants of home prices in Dana Point, California to analyze various factors influencing the real estate market. The results are based on a cross-sectional dataset that incorporates year and month-time dummies to account for temporal trends, as well as spatial variables that capture effects within and between clusters. To address endogeneity issues between square footage and price, parking is employed to instrument square footage and break the reverse causality link. The robustness of the instrument is confirmed through statistical tests, indicating a strong relationship with square footage. Additionally, this study employs the use of Probability models to test Tobit's robustness at estimating the dummy-transformed price variable. Spatial trends are analyzed through fixed effects, random effects, as well as Spatial Autoregressive models absorbing cluster factors, which highlight the differences in price dynamics across various clusters within Dana Point.