House Price Modeling over Heterogeneous Regions with Hierarchical Spatial Functional Analysis

TL;DR

This paper introduces a Hierarchical Spatial Functional Model for house price estimation that captures regional land value discontinuities and sub-communities, improving accuracy over traditional methods.

Contribution

It presents a novel hierarchical spatial functional analysis approach with specialized algorithms for better modeling of complex regional land desirability patterns.

Findings

Reduces mean relative house price estimation error to 6.60%

Handles irregularly shaped regions and land value discontinuities effectively

Outperforms state-of-the-art uniform kernel methods

Abstract

Online real-estate information systems such as Zillow and Trulia have gained increasing popularity in recent years. One important feature offered by these systems is the online home price estimate through automated data-intensive computation based on housing information and comparative market value analysis. State-of-the-art approaches model house prices as a combination of a latent land desirability surface and a regression from house features. However, by using uniformly damping kernels, they are unable to handle irregularly shaped regions or capture land value discontinuities within the same region due to the existence of implicit sub-communities, which are common in real-world scenarios. In this paper, we explore the novel application of recent advances in spatial functional analysis to house price modeling and propose the Hierarchical Spatial Functional Model (HSFM), which decomposes house values into land desirability at both the global scale and hidden local scales as well as the feature regression component. We propose statistical learning algorithms based on finite-element spatial functional analysis and spatial constrained clustering to train our model. Extensive evaluations based on housing data in a major Canadian city show that our proposed approach can reduce the mean relative house price estimation error down to 6.60%.