Spatial Variation of Housing Prices and Its Drivers

---
title: Spatial Variation of Housing Prices and Its Drivers
author: Ming Wang, Hua Li
date: September 2026
abstract: Using housing transactions from 36 cities between 2020 and 2025, we estimate a hedonic price model of location, amenities and building age. A one-standard-deviation increase in rail-transit access raises the unit price by about 6.8%.
bibliography: refs.bib
---

# Introduction

Spatial differences in housing prices are a core question of urban research. Most studies rely on hedonic pricing [@smith2021]; others discuss adjustment factors in the sales comparison approach [@zhang2024].

# Model

Let $P_i$ be the price of dwelling $i$ and $\mathbf{x}_i$ its characteristics. We use the semi-log form

$$
\ln P_i = \beta_0 + \sum_{k=1}^{K} \beta_k x_{ik} + \varepsilon_i
$$ {#eq:hedonic}

where $\beta_k$ in [@eq:hedonic] is the marginal effect of characteristic $k$.

# Results

| Variable            | Coef.   | S.E.   | Sig.   |
|---------------------|--------:|-------:|:------:|
| Rail-transit access | 0.068   | 0.009  | \*\*\* |
| School quality      | 0.052   | 0.011  | \*\*\* |
| Building age (yrs)  | −0.007  | 0.002  | \*\*   |
| Floor-area ratio    | −0.015  | 0.006  | \*     |

: Hedonic model estimates {#tbl:est}

# References