Next Article in Journal
Entropy as a Metric Generator of Dissipation in Complete Metriplectic Systems
Next Article in Special Issue
Entropy Base Estimation of Moisture Content of the Top 10-m Unsaturated Soil for the Badain Jaran Desert in Northwestern China
Previous Article in Journal
Thermal Analysis of Shell-and-Tube Thermoacoustic Heat Exchangers
Previous Article in Special Issue
Characterization of Seepage Velocity beneath a Complex Rock Mass Dam Based on Entropy Theory
Article Menu

Export Article

Open AccessArticle
Entropy 2016, 18(8), 303; doi:10.3390/e18080303

A Geographically Temporal Weighted Regression Approach with Travel Distance for House Price Estimation

1
Research Center of Government GIS, Chinese Academy of Surveying and Mapping, No. 28 Lianhuachi West Road, Haidian District, Beijing 100830, China
2
School of Resource and Environmental Science, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Academic Editor: Raúl Alcaraz Martínez
Received: 12 May 2016 / Revised: 10 August 2016 / Accepted: 10 August 2016 / Published: 16 August 2016
(This article belongs to the Special Issue Applications of Information Theory in the Geosciences)
View Full-Text   |   Download PDF [1217 KB, uploaded 16 August 2016]   |  

Abstract

Previous studies have demonstrated that non-Euclidean distance metrics can improve model fit in the geographically weighted regression (GWR) model. However, the GWR model often considers spatial nonstationarity and does not address variations in local temporal issues. Therefore, this paper explores a geographically temporal weighted regression (GTWR) approach that accounts for both spatial and temporal nonstationarity simultaneously to estimate house prices based on travel time distance metrics. Using house price data collected between 1980 and 2016, the house price response and explanatory variables are then modeled using both the GWR and the GTWR approaches. Comparing the GWR model with Euclidean and travel distance metrics, the GTWR model with travel distance obtains the highest value for the coefficient of determination ( R 2 ) and the lowest values for the Akaike information criterion (AIC). The results show that the GTWR model provides a relatively high goodness of fit and sufficient space-time explanatory power with non-Euclidean distance metrics. The results of this study can be used to formulate more effective policies for real estate management. View Full-Text
Keywords: geographically and temporally weighted regression; travel time; housing prices geographically and temporally weighted regression; travel time; housing prices
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Liu, J.; Yang, Y.; Xu, S.; Zhao, Y.; Wang, Y.; Zhang, F. A Geographically Temporal Weighted Regression Approach with Travel Distance for House Price Estimation. Entropy 2016, 18, 303.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top