Next Article in Journal
Education and Training in Applied Remote Sensing in Africa: The ARCSSTE-E Experience
Previous Article in Journal
Performance Testing on Marker Clustering and Heatmap Visualization Techniques: A Comparative Study on JavaScript Mapping Libraries
Open AccessArticle

Modeling Housing Rent in the Atlanta Metropolitan Area Using Textual Information and Deep Learning

1
Department of Geography, 2855 Main Drive, Fort Worth, TX 76109, USA
2
Statistics and Mathematics School, Yunnan University of Finance and Economics, Kunming 650221, China
3
Department of Computer Science, Eastern Michigan University, Ypsilanti, MI 48197, USA
4
Department of Landscape Architecture and Urban Planning, Texas A&M University, College Station, TX 77843, USA
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(8), 349; https://doi.org/10.3390/ijgi8080349
Received: 28 May 2019 / Revised: 22 July 2019 / Accepted: 25 July 2019 / Published: 2 August 2019
  |  
PDF [2453 KB, uploaded 2 August 2019]
  |  

Abstract

The rental housing market plays a critical role in the United States real estate market. In addition, rent changes are also indicators of urban transformation and social phenomena. However, traditional data sources for market rent prediction are often inaccurate or inadequate at covering large geographies. With the development of housing information exchange platforms such as Craigslist, user-generated rental listings now provide big data that cover wide geographies and are rich in textual information. Given the importance of rent prediction in urban studies, this study aims to develop and evaluate models of rental market dynamics using deep learning approaches on spatial and textual data from Craigslist rental listings. We tested a number of machine learning and deep learning models (e.g., convolutional neural network, recurrent neural network) for the prediction of rental prices based on data collected from Atlanta, GA, USA. With textual information alone, deep learning models achieved an average root mean square error (RMSE) of 288.4 and mean absolute error (MAE) of 196.8. When combining textual information with location and housing attributes, the integrated model achieved an average RMSE of 227.9 and MAE of 145.4. These approaches can be applied to assess the market value of rental properties, and the prediction results can be used as indicators of a variety of urban phenomena and provide practical references for home owners and renters. View Full-Text
Keywords: rental price; spatial modeling; geographic information systems; machine learning; Atlanta rental price; spatial modeling; geographic information systems; machine learning; Atlanta
Figures

Figure 1

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

Share & Cite This Article

MDPI and ACS Style

Zhou, X.; Tong, W.; Li, D. Modeling Housing Rent in the Atlanta Metropolitan Area Using Textual Information and Deep Learning. ISPRS Int. J. Geo-Inf. 2019, 8, 349.

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]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top