Mapping fine‐scale urban housing prices by fusing remotely sensed imagery and social media data

Yao, Yao; Zhang, Jinbao; Hong, Ye; Liang, Haolin; He, Jialyu

doi:10.1111/tgis.12330

Cited by 61 publications

(46 citation statements)

References 56 publications

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“…In the last decade, housing prices have become one of the top issues in economic development and for determining whether urban residents can live a better life [1][2][3]. The rapid growth of housing prices and spatial differentiation greatly concern managers, scholars, developers and residents [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Understanding the Effects of Influential Factors on Housing Prices by Combining Extreme Gradient Boosting and a Hedonic Price Model (XGBoost-HPM)

Jiang²,

Ke³

et al. 2021

Land

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The characteristics of housing and location conditions are the main drivers of spatial differences in housing prices, which is a topic attracting high interest in both real estate and geography research. One of the most popular models, the hedonic price model (HPM), has limitations in identifying nonlinear relationships and distinguishing the importance of influential factors. Therefore, extreme gradient boosting (XGBoost), a popular machine learning technology, and the HPM were combined to analyse the comprehensive effects of influential factors on housing prices. XGBoost was employed to identify the importance order of factors and HPM was adopted to reveal the value of the original non-market priced influential factors. The results showed that combining the two models can lead to good performance and increase understanding of the spatial variations in housing prices. Our work found that (1) the five most important variables for Shenzhen housing prices were distance to city centre, green view index, population density, property management fee and economic level; (2) space quality at the human scale had important effects on housing prices; and (3) some traditional factors, especially variables related to education, should be modified according to the development of the real estate market. The results showed that the demonstrated multisource geo-tagged data fusion framework, which integrated XGBoost and HPM, is practical and supports a comprehensive understanding of the relationships between housing prices and influential factors. The findings in this article provide essential implications for informing equitable housing policies and designing liveable neighbourhoods.

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Section: Introductionmentioning

confidence: 99%

Understanding the Effects of Influential Factors on Housing Prices by Combining Extreme Gradient Boosting and a Hedonic Price Model (XGBoost-HPM)

Jiang²,

Ke³

et al. 2021

Land

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show abstract

“…The rental housing market is a very important part of the real estate market, and has received considerable attention from scholars. Many previous studies focused on identifying the driving factors of rental prices rather than modeling, predicting, and mapping the spatial distribution of prices [36]. Practically, models that accurately predict rental price can help property owners' better price their rental properties and assist tenants in finding places to live with reasonable prices.…”

Section: Discussionmentioning

confidence: 99%

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

Zhou

Tong

2019

IJGI

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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.

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“…These 4 error metrics evaluate the absolute and percentage estimation error together. The mentioned 6 metrics complement each other and provide a comprehensive assessment of the proposed framework and the produced data [69].…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Mapping Multi-Temporal Population Distribution in China from 1985 to 2010 Using Landsat Images via Deep Learning

Zhuang

Yan

et al. 2021

Remote Sensing

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Fine knowledge of the spatiotemporal distribution of the population is fundamental in a wide range of fields, including resource management, disaster response, public health, and urban planning. The United Nations’ Sustainable Development Goals also require the accurate and timely assessment of where people live to formulate, implement, and monitor sustainable development policies. However, due to the lack of appropriate auxiliary datasets and effective methodological frameworks, there are rarely continuous multi-temporal gridded population data over a long historical period to aid in our understanding of the spatiotemporal evolution of the population. In this study, we developed a framework integrating a ResNet-N deep learning architecture, considering neighborhood effects with a vast number of Landsat-5 images from Google Earth Engine for population mapping, to overcome both the data and methodology obstacles associated with rapid multi-temporal population mapping over a long historical period at a large scale. Using this proposed framework in China, we mapped fine-scale multi-temporal gridded population data (1 km × 1 km) of China for the 1985–2010 period with a 5-year interval. The produced multi-temporal population data were validated with available census data and achieved comparable performance. By analyzing the multi-temporal population grids, we revealed the spatiotemporal evolution of population distribution from 1985 to 2010 in China with the characteristic of concentration of the population in big cities and the contraction of small- and medium-sized cities. The framework proposed in this study demonstrates the feasibility of mapping multi-temporal gridded population distribution at a large scale over a long period in a timely and low-cost manner, which is particularly useful in low-income and data-poor areas.

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Mapping fine‐scale urban housing prices by fusing remotely sensed imagery and social media data

Cited by 61 publications

References 56 publications

Understanding the Effects of Influential Factors on Housing Prices by Combining Extreme Gradient Boosting and a Hedonic Price Model (XGBoost-HPM)

Understanding the Effects of Influential Factors on Housing Prices by Combining Extreme Gradient Boosting and a Hedonic Price Model (XGBoost-HPM)

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

Mapping Multi-Temporal Population Distribution in China from 1985 to 2010 Using Landsat Images via Deep Learning

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