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Reinvigorating Research on Housing Inequalities and Housing Price Mechanism Using Emerging Data and Technologies

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A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Urban Contexts and Urban-Rural Interactions".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 58267

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Special Issue Editors

Prof. Dr. Shiliang Su

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Guest Editor

Department of GI Science and Cartography, College of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
Interests: geocomputational social sciences; land use modeling; thematic cartography

Prof. Dr. Shenjing He

E-Mail Website
Guest Editor

Department of Urban Planning and Design, The Social Infrastructure for Equity and Wellbeing (SIEW) Lab, The University of Hong Kong, Hong Kong, China
Interests: urban redevelopment/gentrification; policy mobility and entrepreneurial urbanism; rural-urban migration and informal housing; neighborhood governance; health geography

Dr. Monika Kuffer

E-Mail Website
Guest Editor

Faculty of Geo-Information Science and Earth Observation (ITC) of the University of Twente, 7514 AE Enschede, The Netherlands
Interests: urban remote sensing; urban modelling; spatial statistics; urban planning; slum mapping; deprived area mapping
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the ground-breaking advances in the Spatial Information Communication Techniques (SICTs), the Artificial Intelligence Techniques (AITs), the High-Performance Computing Techniques (HPCTs), Sensors and Wearable Devices (SWDs) and the Internet of Things (IoT), an inexhaustible supply of new, open, and big data has revolutionized the world over the recent decade. A shared vision has been formed in the spheres of politics, academia, and business on the strategic significance of emerging data and technologies for building more inclusive, vibrant, and sustainable future cities (Chattopadhyay et al., 2020; Porciello et al., 2020). Tackling the knotty problems of land and housing is a central task for urban planning and governance, as urban development is rooted in the nexus of land and capital. The importance of affordable housing has in fact been acknowledged by the Sustainable Development Goals (SDGs 11).

A myriad of studies have responded to the emerging data and technologies in almost all fields of housing research. The availability of new data and technologies has reinvigorated the research on classical housing issues by offering new empirical evidence, strengthening analytical rigor, and even enabling new discoveries in housing theories (Abitbol and Karsai, 2020; Boeing, 2020; He et al., 2019; Hu et al., 2019; Gallin et al., 2021; Ibrahim et al., 2019; Soman et al., 2020; Su et al., 2021; Ying et al., 2021). These studies, however, could be misleading if over-relying on data-driven approaches without a close engagement with theoretical debates. The absence of a solid theoretical foundation could lead to inconsistent, fragmented, and incomparable conclusions, posing a major obstacle to the multilateral dialogues among practitioners, researchers, technicians, and policymakers. Most importantly, the SDGs have presented high fragility during the COVID-19 pandemic worldwide, two-thirds of which are now unlikely to be achieved (Naidoo and Fisher, 2020). It is the right time to rethink sustainable pathways for combating the worsening housing inequalities.

Therefore, this Special Issue calls for papers employing innovative data sources and methodologies to reenergize the classical research topics of housing inequalities and housing price mechanisms and to advance the theorization of housing studies (on the Global South and North) in the era of big data and smart technologies. We invite contributions from various disciplines such as Land Management, Geography, Urban Planning, Real Estate, GIScience, Economics, and Computer Science. Potential themes include but are not limited to:

The role of Big Data and AITs in understanding dynamics (e.g., housing price, housing rent and land rent) and spatiotemporal patterns of housing markets (e.g., market segmentation, housing affordability and housing opportunities);
Capitalization effect of neighborhood characteristics (e.g., population composition, built environment, diversity, and homogeneity) and urban amenities (e.g., education, hospital, landscape, and public transport);
New indicators of housing divide (e.g., address, place name, streetscape. and 3D Cadastre);
Opportunities and challenges of data fusion from multiple sources for identifying housing inequalities;
Socioeconomic consequences of housing differentiation (e.g., health inequalities, urban poverty and human mobility).

References

Abitbol, J.L., Karsai, M. Interpretable socioeconomic status inference from aerial imagery through urban patterns. Nature Machine Intelligence, 2020, 2, 684-692.

Boeing, G. Online rental housing market representation and the digital reproduction of urban inequality. Environment and Planning A, 2020, 52, 449-468.

Chattopadhyay, A.K., Kumar, T.K., Rice, I. A social engineering model for poverty alleviation. Nature Communication, 2020, 11, 6345.

Gallin, J., Molloy, R., Nielsen, E., Smith, P., Sommer, K. Measuring aggregate housing wealth: New insights from machine learning. Journal of Housing Economics, 2021, 51, 101734.

He, S., Wang, D., Webster, C., Chau, K.W. Property rights with price tags? Pricing uncertainties in the production, transaction and consumption of China’s small property right housing. Land Use Policy, 2019, 81, 424-433.

Hu, L., He, S., Han, Z., Xiao, H., Su, S., Weng, M., Cai, Z. Monitoring housing rental prices based on social media: an integrated approach of machine-learning algorithms and hedonic modeling to inform equitable housing policies. Land Use Policy, 2019, 82, 657-673.

Ibrahim, M.R., Titheridge, H., Cheng, T., Haworth, J. predictSLUMS: A new model for identifying and predicting informal settlements and slums in cities from street intersections using machine learning. Computers, Environment and Urban Systems, 2019, 76, 31-56.

Naidoo, R., Fisher, B. Reset sustainable development goals for a pandemic world. Nature, 2020, 583, 198-201.

Porciello, J., Ivanina, M., Islam, M. et al. Accelerating evidence-informed decision-making for the Sustainable Development Goals using machine learning. Nature Machine Intelligence, 2, 559-565.

Soman, S., Beukes, A., Nederhood, C., Marchio, N., Bettencourt, L.M.A. Worldwide detection of informal settlements via topological analysis of crowdsourced digital maps. International Journal of Geo-Information, 2020, 9, 685.

Su, S., He, S., Sun, C., Zhang, H., Hu, L., Kang, M. Do landscape amenities impact private housing rental prices? A hierarchical hedonic modeling approach based on semantic and sentimental analysis of online housing advertisements across five Chinese megacities. Urban Forestry & Urban Greening，2021, 126968.

Ying, Y., Koeva, M., Kuffer, M., Asiama, K.O., Li, X., Zevenbergen, J. Making the third dimension (3D) explicit in Hedonic price modelling: A case study of Xi’an, China. Land, 2021, 10, 24.

Prof. Dr. Shiliang Su
Prof. Dr. Shenjing He
Dr. Monika Kuffer
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Land is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Housing market
Machine learning
Big data
Urban analyst
Social inequalities
Affordable housing
Artificial Intelligence

Published Papers (12 papers)

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Research

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32 pages, 11831 KiB

Open AccessArticle

Housing Price Prediction Using Machine Learning Algorithms in COVID-19 Times

by Raul-Tomas Mora-Garcia, Maria-Francisca Cespedes-Lopez and V. Raul Perez-Sanchez

Land 2022, 11(11), 2100; https://0-doi-org.brum.beds.ac.uk/10.3390/land11112100 - 21 Nov 2022

Cited by 17 | Viewed by 13823

Abstract

Machine learning algorithms are being used for multiple real-life applications and in research. As a consequence of digital technology, large structured and georeferenced datasets are now more widely available, facilitating the use of these algorithms to analyze and identify patterns, as well as to make predictions that help users in decision making. This research aims to identify the best machine learning algorithms to predict house prices, and to quantify the impact of the COVID-19 pandemic on house prices in a Spanish city. The methodology addresses the phases of data preparation, feature engineering, hyperparameter training and optimization, model evaluation and selection, and finally model interpretation. Ensemble learning algorithms based on boosting (Gradient Boosting Regressor, Extreme Gradient Boosting, and Light Gradient Boosting Machine) and bagging (random forest and extra-trees regressor) are used and compared with a linear regression model. A case study is developed with georeferenced microdata of the real estate market in Alicante (Spain), before and after the pandemic declaration derived from COVID-19, together with information from other complementary sources such as the cadastre, socio-demographic and economic indicators, and satellite images. The results show that machine learning algorithms perform better than traditional linear models because they are better adapted to the nonlinearities of complex data such as real estate market data. Algorithms based on bagging show overfitting problems (random forest and extra-trees regressor) and those based on boosting have better performance and lower overfitting. This research contributes to the literature on the Spanish real estate market by being one of the first studies to use machine learning and microdata to explore the incidence of the COVID-19 pandemic on house prices. Full article

(This article belongs to the Special Issue Reinvigorating Research on Housing Inequalities and Housing Price Mechanism Using Emerging Data and Technologies)

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29 pages, 38756 KiB

Open AccessArticle

The Perception of the Vertical Dimension (3D) through the Lens of Different Stakeholders in the Property Market of China

by Yue Ying, Mila Koeva, Monika Kuffer, Kwabena Obeng Asiama, Xia Li and Jaap Zevenbergen

Land 2022, 11(2), 312; https://0-doi-org.brum.beds.ac.uk/10.3390/land11020312 - 19 Feb 2022

Cited by 2 | Viewed by 2780

Abstract

China has experienced fast urbanization with a growing urban population, which has inevitably led to the adoption of a vertical housing style with high-rise buildings. However, how people subjectively perceive the vertical dimension (3D) in the property market is neither adequately documented nor well understood. The 3D perception helps us to understand a myriad of social and psychological effects of living in high-rise buildings. We organized and conducted semi-structured expert interviews, focus groups, and the circulation and compilation of questionnaires in Xi’an, China, to investigate how different stakeholders in the property market perceive 3D. The results show that: (1) real estate developers do not adjust property prices for specific 3D factors, and the local government does not consider 3D in housing policies; (2) the current status of 3D modeling in Xi’an is still in the embryonic state; (3) 3D factors are highly valued by buyers but not well-understood by real estate developers and local government. In addition, 3D factors score higher than horizontal (2D) factors (1.12 to 0.88). Gender and age groups do not influence housing preferences concerning 2D and 3D factors. These findings provide valuable insights for real estate developers concerning pricing policies and the local government concerning housing policies. In the future, 3D perceptions and factors should be prioritized in order to improve urban infrastructure and ensure the increased availability of, and fair public access to welfare related to 3D in urban areas. Full article

(This article belongs to the Special Issue Reinvigorating Research on Housing Inequalities and Housing Price Mechanism Using Emerging Data and Technologies)

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28 pages, 2233 KiB

Open AccessArticle

Exploring a Pricing Model for Urban Rental Houses from a Geographical Perspective

by Hang Shen, Lin Li, Haihong Zhu, Yu Liu and Zhenwei Luo

Land 2022, 11(1), 4; https://0-doi-org.brum.beds.ac.uk/10.3390/land11010004 - 21 Dec 2021

Cited by 5 | Viewed by 2968

Abstract

Models for estimating urban rental house prices in the real estate market continue to pose a challenging problem due to the insufficiency of algorithms and comprehensive perspectives. Existing rental house price models based on either the geographically weighted regression (GWR) or deep-learning methods can hardly predict very satisfactory prices, since the rental house prices involve both complicated nonlinear characteristics and spatial heterogeneity. The linear-based GWR model cannot characterize the nonlinear complexity of rental house prices, while existing deep-learning methods cannot explicitly model the spatial heterogeneity. This paper proposes a fully connected neural network–geographically weighted regression (FCNN–GWR) model that combines deep learning with GWR and can handle both of the problems above. In addition, when calculating the geographical location of a house, we propose a set of locational and neighborhood variables based on the quantities of nearby points of interests (POIs). Compared with traditional locational and neighborhood variables, the proposed “quantity-based” locational and neighborhood variables can cover more geographic objects and reflect the locational characteristics of a house from a comprehensive geographical perspective. Taking four major Chinese cities (Wuhan, Nanjing, Beijing, and Xi’an) as study areas, we compare the proposed method with other commonly used methods, and this paper presents a more precise estimation model for rental house prices. The method proposed in this paper may serve as a useful reference for individuals and enterprises in their transactions relevant to rental houses, and for the government in terms of the policies and positions of public rental housing. Full article

(This article belongs to the Special Issue Reinvigorating Research on Housing Inequalities and Housing Price Mechanism Using Emerging Data and Technologies)

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16 pages, 946 KiB

Open AccessArticle

Difference in Housing Finance Usage and Its Impact on Housing Wealth Inequality in Urban China

by Shan Yu and Can Cui

Land 2021, 10(12), 1404; https://0-doi-org.brum.beds.ac.uk/10.3390/land10121404 - 19 Dec 2021

Cited by 2 | Viewed by 2708

Abstract

With the increasing importance of financial loans in home purchases in urban China, the role of housing loans in the accumulation of housing wealth needs to be unraveled. Using the data from the 2017 China Household Finance Survey (CHFS), this study investigates the use of housing loans and their impact on housing wealth inequality. It has been found that people with higher socioeconomic status and institutional advantages benefit more from housing provident fund loans and are more likely to fully invoke different financing channels to accumulate housing wealth. On the contrary, disadvantaged groups have to resort to costly market-based mortgages to finance their home purchases. This leads them to fall further behind in housing wealth accumulation. The spatial stratification of housing wealth accompanying the urban hierarchy was also observed and found to be closely linked to the type of housing loans. In this increasingly financialized era, relying on financial instruments in the process of household asset accumulation may further amplify the existing wealth inequality among social groups. Full article

(This article belongs to the Special Issue Reinvigorating Research on Housing Inequalities and Housing Price Mechanism Using Emerging Data and Technologies)

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25 pages, 1589 KiB

Open AccessArticle

Spatial Interactions in Business and Housing Location Models

by Katarzyna Kopczewska, Mateusz Kopyt and Piotr Ćwiakowski

Land 2021, 10(12), 1348; https://0-doi-org.brum.beds.ac.uk/10.3390/land10121348 - 07 Dec 2021

Cited by 4 | Viewed by 3827

Abstract

The paper combines theoretical models of housing and business locations and shows that they have the same determinants. It evidences that classical, behavioural, new economic geography, evolutionary and co-evolutionary frameworks apply simultaneously, and one should consider them jointly when explaining urban structure. We use quantitative tools in a theory-guided factors induction approach to show the complexity of location models. The paper discusses and measures spatial phenomena as distance-decaying gradients, spatial discontinuities, densities, spillovers, spatial interactions, agglomerations, and as multimodal processes. We illustrate the theoretical discussion with an empirical case of interacting point-patterns for business, housing, and population. The analysis reveals strong links between housing valuation and business location and profitability, accompanied by the related spatial phenomena. It also shows that assumptions concerning unimodal spatial urban structure, the existence of rational maximisers, distance-decaying externalities, and a single pattern of behaviour, do not hold. Instead, the reality entails consideration of multimodality, a mixture of maximisers and satisfiers, incomplete information, appearance of spatial interactions, feed-back loops, as well as the existence of persistence of behaviour, with slow and costly adjustments of location. Full article

(This article belongs to the Special Issue Reinvigorating Research on Housing Inequalities and Housing Price Mechanism Using Emerging Data and Technologies)

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15 pages, 4406 KiB

Open AccessArticle

Measuring the Differences of Public Health Service Facilities and Their Influencing Factors

by Shihang Fu, Yaolin Liu and Ying Fang

Land 2021, 10(11), 1225; https://0-doi-org.brum.beds.ac.uk/10.3390/land10111225 - 11 Nov 2021

Cited by 8 | Viewed by 1741

Abstract

The equitable distribution of public health facilities is a major concern of urban planners. Previous studies have explored the balance and fairness of various medical resource distributions using the accessibility of in-demand public medical service facilities while ignoring the differences in the supply of public medical service facilities. First aid data with location information and patient preference information can reflect the ability of each hospital and the health inequities in cities. Determining which factors affect the measured differences in public medical service facilities and how to alter these factors will help researchers formulate targeted policies to solve the current resource-balance situation of the Ministry of Public Health. In this study, we propose a method to measure the differences in influence among hospitals based on actual medical behavior and use geographically weighted regression (GWR) to analyze the spatial correlations among the location, medical equipment, medical ability, and influencing factors of each hospital. The results show that Wuhan presents obvious health inequality, with the high-grade hospitals having spatial agglomeration in the city-center area, while the number and quality of hospitals in the peripheral areas are lower than those in the central area; thus, the hospitals in these peripheral areas need to be further improved. The method used in this study can measure differences in the influence of public medical service facilities, and the results are consistent with the measured differences at hospital level. Hospital influence is not only related to the equipment and medical ability of each hospital but is also affected by location factors. This method illustrates the necessity of conducting more empirical research on the public medical service supply to provide a scientific basis for formulating targeted policies from a new perspective. Full article

(This article belongs to the Special Issue Reinvigorating Research on Housing Inequalities and Housing Price Mechanism Using Emerging Data and Technologies)

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17 pages, 4361 KiB

Open AccessArticle

Evaluation on the Internal Public Space Quality in Affordable Housing Based on Multi-Source Data and IPA Analysis

by Jie Zhao, Zhenghong Peng, Lingbo Liu, Yang Yu and Yan Yu

Land 2021, 10(10), 1000; https://0-doi-org.brum.beds.ac.uk/10.3390/land10101000 - 23 Sep 2021

Cited by 4 | Viewed by 2412

Abstract

Much affordable housing has poor accessibility to external urban public space facilities because of its suburb location, which makes the residents’ daily life and social activities mainly depend on the internal public space of the community. Such affordable housing needs urgent upgrading of the internal public space based on the thorough understanding of the low-income residents’ demand ranking. The internal spaces’ transformation will significantly improve the living environment and the quality of residents’ life, and it also provides a way to promote social equity and sustainable urban development. By using the multi-source data and the two-step floating catchment area method, this paper selects typical affordable housing, which has poor accessibility to external urban public space, as our case study. After establishing the evaluation index system, IPA (Importance and Performance Analysis) is used to calculate the quadrant value of each index so as to clarify the upgrading urgency indexes from the residents’ demand for internal public space of affordable housing. Studies have shown that tables, chairs and pavilions, pedestrian systems, retail commercial facilities, medical and health facilities, and recreational space have the strongest urgency for upgrading; fitness facilities, exercise space, barrier-free access, guidance signs and parking lot design are the next most urgent indexes; Recreational facilities, entrances/exits of the residential area, green space in front of t residential buildings, and cultural facilities all have general urgency for upgrading. Full article

(This article belongs to the Special Issue Reinvigorating Research on Housing Inequalities and Housing Price Mechanism Using Emerging Data and Technologies)

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21 pages, 1789 KiB

Open AccessArticle

The Heightened ‘Security Zone’ Function of Gated Communities during the COVID-19 Pandemic and the Changing Housing Market Dynamic: Evidence from Beijing, China

by Ling Li, Wayne Xinwei Wan and Shenjing He

Land 2021, 10(9), 983; https://0-doi-org.brum.beds.ac.uk/10.3390/land10090983 - 17 Sep 2021

Cited by 16 | Viewed by 5248

Abstract

The ongoing COVID-19 pandemic has left a strong imprint on many aspects of urban life. Gated communities (GCs) in China are less commonly perceived as a negative and segregated urban form of community compared to other contexts, owing to their wide variety and relative openness. Yet, the enhanced security zone function and the popularity of GCs, along with the heightened segregation and exclusion effects, mean they are most likely to emerge in post-pandemic urban China because of the perceived effectiveness of GCs in preventing health risks by excluding outsiders during the pandemic. Drawing on empirical data from Beijing, this research presents strong evidence for a strengthened perceived ‘security zone’ effect of GCs during the pandemic. Given that rigid pandemic control measures were organized at the community level, a large-scale household survey in Beijing suggests that residents commonly recognise the effectiveness of GCs in security control and show a strong preference for GCs over open communities after the pandemic, even though there is a lack of direct evidence of reduced COVID-19 risk in GCs. The heightened perceived ‘security zone’ function of GCs has shown a significant impact on the housing market, evidenced by an increase of 2% in the housing prices for GCs, compared with those of open communities. The rising popularity of GCs is also evidenced by a significant increase in property viewings by potential homebuyers and smaller price discounts in actual transactions in gated communities vis-à-vis open communities. We argue that the rising risk-averse sentiment in the post-pandemic era has given rise to the popularity of GCs. This study provides timely and fresh insights into the changing meaning of GCs in post-pandemic China. Full article

(This article belongs to the Special Issue Reinvigorating Research on Housing Inequalities and Housing Price Mechanism Using Emerging Data and Technologies)

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33 pages, 6611 KiB

Open AccessArticle

Spatial Inequality in China’s Housing Market and the Driving Mechanism

by Sidong Zhao, Kaixu Zhao and Ping Zhang

Land 2021, 10(8), 841; https://0-doi-org.brum.beds.ac.uk/10.3390/land10080841 - 11 Aug 2021

Cited by 29 | Viewed by 3580

Abstract

Housing inequality is a widespread phenomenon around the world, and it varies widely across countries and regions. The housing market is naturally spatial in its attributes, and with the transformation of China’s urbanization, industrialization, and globalization, the spatial inequality in the housing market is increasingly severe. According to the geospatial differences in the housing market supply, demand, and price, and by integrating the influencing factors of economic, social, innovation, facility environment, and structural adjustment, this paper constructs a “spatial–supply–demand–price” integrated housing market inequality research framework based on the methods of CV, GI, and Geodetector, and it empirically studies the spatial inequality of provincial housing markets in China. The findings show that the spatial inequality in China’s housing market is significant and becomes increasingly serious. According to the study, we have confirmed the following. (1) Different factors vary greatly in influence, and they can be classified into three types, that is, “Key factors”, “Important factors”, and “Auxiliary factors”. (2) The spatial inequalities in housing supply, demand, and price vary widely in their driving mechanisms, but factors such as the added value of the tertiary industry, number of patents granted, and revenue affect all these three at the same time and have a comprehensive influence on the development and evolution of spatial inequalities in the housing market. (3) All the factors are bifactor-enhanced or non-linearly enhanced in relationships between every pair, and they are classified into three categories of high, medium, and low according to the mean of interacting forces; in particular, the factors of GDP, expenditure, permanent resident population, number of medical beds, and full-time equivalent of R&D personnel are in a stronger interaction with other factors. (4) Based on housing supply, demand, price, and their coordination, 31 provinces are classified into four types of policy zones, and the driving mechanisms of spatial inequalities in the housing market are further applied to put forward suggestions on policy design, which provides useful references for China and other countries to deal with housing spatial inequality. Full article

(This article belongs to the Special Issue Reinvigorating Research on Housing Inequalities and Housing Price Mechanism Using Emerging Data and Technologies)

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16 pages, 13076 KiB

Open AccessArticle

Identifying Urban Poverty Using High-Resolution Satellite Imagery and Machine Learning Approaches: Implications for Housing Inequality

by Guie Li, Zhongliang Cai, Yun Qian and Fei Chen

Land 2021, 10(6), 648; https://0-doi-org.brum.beds.ac.uk/10.3390/land10060648 - 18 Jun 2021

Cited by 13 | Viewed by 3476

Abstract

Enriching Asian perspectives on the rapid identification of urban poverty and its implications for housing inequality, this paper contributes empirical evidence about the utility of image features derived from high-resolution satellite imagery and machine learning approaches for identifying urban poverty in China at the community level. For the case of the Jiangxia District and Huangpi District of Wuhan, image features, including perimeter, line segment detector (LSD), Hough transform, gray-level cooccurrence matrix (GLCM), histogram of oriented gradients (HoG), and local binary patterns (LBP), are calculated, and four machine learning approaches and 25 variables are applied to identify urban poverty and relatively important variables. The results show that image features and machine learning approaches can be used to identify urban poverty with the best model performance with a coefficient of determination, R², of 0.5341 and 0.5324 for Jiangxia and Huangpi, respectively, although some differences exist among the approaches and study areas. The importance of each variable differs for each approach and study area; however, the relatively important variables are similar. In particular, four variables achieved relatively satisfactory prediction results for all models and presented obvious differences in varying communities with different poverty levels. Housing inequality within low-income neighborhoods, which is a response to gaps in wealth, income, and housing affordability among social groups, is an important manifestation of urban poverty. Policy makers can implement these findings to rapidly identify urban poverty, and the findings have potential applications for addressing housing inequality and proving the rationality of urban planning for building a sustainable society. Full article

(This article belongs to the Special Issue Reinvigorating Research on Housing Inequalities and Housing Price Mechanism Using Emerging Data and Technologies)

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15 pages, 32133 KiB

Open AccessArticle

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

by Sheng Li, Yi Jiang, Shuisong Ke, Ke Nie and Chao Wu

Land 2021, 10(5), 533; https://0-doi-org.brum.beds.ac.uk/10.3390/land10050533 - 18 May 2021

Cited by 15 | Viewed by 4781

Abstract

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. Full article

(This article belongs to the Special Issue Reinvigorating Research on Housing Inequalities and Housing Price Mechanism Using Emerging Data and Technologies)

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Review

Jump to: Research

30 pages, 2048 KiB

Open AccessReview

The Research Development of Hedonic Price Model-Based Real Estate Appraisal in the Era of Big Data

by Cankun Wei, Meichen Fu, Li Wang, Hanbing Yang, Feng Tang and Yuqing Xiong

Land 2022, 11(3), 334; https://0-doi-org.brum.beds.ac.uk/10.3390/land11030334 - 24 Feb 2022

Cited by 25 | Viewed by 8013

Abstract

In the era of big data, advances in relevant technologies are profoundly impacting the field of real estate appraisal. Many scholars regard the integration of big data technology as an inevitable future trend in the real estate appraisal industry. In this paper, we summarize 124 studies investigating the use of big data technology to optimize real estate appraisal through the hedonic price model (HPM). We also list a variety of big data resources and key methods widely used in the real estate appraisal field. On this basis, the development of real estate appraisal moving forward is analyzed. The results obtained in the current studies are as follows: First, the big data resources currently applied to real estate appraisal include more than a dozen big data types from three data sources; the internet, remote sensing, and the Internet of things (IoT). Additionally, it was determined that web crawler technology represents the most important data acquisition method. Second, methods such as data pre-processing, spatial modeling, Geographic information system (GIS) spatial analysis, and the evolving machine learning methods with higher valuation accuracy were successfully introduced into the HPM due to the features of real estate big data. Finally, although the application of big data has greatly expanded the amount of available data and feature dimensions, this has caused a new problem: uneven data quality. Uneven data quality can reduce the accuracy of appraisal results, and, to date, insufficient attention has been paid to this issue. Future research should pay greater attention to the data integration of multi-source big data and absorb the applications developed in other disciplines. It is also important to combine various methods to form a new united evaluation model based on taking advantage of, and avoiding shortcomings to compensate for, the mechanism defects of a single model. Full article

(This article belongs to the Special Issue Reinvigorating Research on Housing Inequalities and Housing Price Mechanism Using Emerging Data and Technologies)

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