Using Geographic Information and Point of Interest to Estimate Missing Second-Hand Housing Price of Residential Area in Urban Space

Tang, Jiabo; Liu, Zhicheng; Wang, Yuran; Yang, Junyan; Qiao, Wenxiao

doi:10.1109/isc2.2018.8656965

Cited by 7 publications

(6 citation statements)

References 11 publications

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“…Considering the market conditions in Olsztyn (the average price of 1m2 of an apartment is about 6000 PLN), the maximum impact of the proximity of POIs may be close to 25% of the average price. The results obtained confirm the studies conducted so far (Tang et al, 2018). While there is no doubt that in places where the density of POIs is higher, prices are also higher, the assumption of causality should be treated with some distance.…”

Section: Resultssupporting

confidence: 88%

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Points of Interest and Housing Prices

Cellmer¹

2023

Real Estate Management and Valuation

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Points of Interest (POI) are an inherent element of the urban landscape, and their number and density reflect, among other things, the degree of urbanization and the city’s spatial structure. The very presence of POI in the closest vicinity of a residential property may indirectly or directly affect housing value. This paper aims to demonstrate the usefulness of POI density information of different categories in assessing the quality of a property’s immediate surroundings. While the mere presence of POIs in the nearest neighborhood may affect real estate prices, the influence of specific categories may not necessarily be positive. Therefore, the study attempted to classify POIs and determine their importance in the price formation process using spatial regression models. The results indicate that a high density of POIs in the immediate area is a stimulant for housing prices. The detailed analysis indicated that only some POI categories might be related to transaction prices, while in certain situations, some POI categories may negatively impact prices.

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Section: Resultssupporting

confidence: 88%

“…Previous research confirms the high correlation between POI density and housing prices (Tang et al, 2018). The possibility of using POI information to define price determinants is also presented by Wu et al (2018), who uses this data to build a geographically weighted regression model.…”

Section: Realmentioning

confidence: 59%

Points of Interest and Housing Prices

Cellmer¹

2023

Real Estate Management and Valuation

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“…In previous studies, authors usually approached this problem intuitively. For example, (Chen & Clark, 2013) indicate that the acceptable distance of walking to a food shop is approximately 800 m. On the other hand, (Tang et al, 2018) adopt one kilometre as the bandwidth in the kernel function to determine the scope of impact of POI points while believing that this distance should be 700 m. To specify the bandwidth, (Cellmer et al, 2019(Cellmer et al, , 2020 suggest utilising the range of semivariograms of residential property prices, which indicates a distance of about 200 m. Conducting a rational evaluation of the concept of proximity regarding going on foot, we assumed that this distance should correspond to a five-minute walk, i.e., approximately 250 m. Therefore, we adopted such a distance as the bandwidth. Having assessed the density in each point corresponding to a transaction, we read the indicator of POI density in total and for each category separately.…”

Section: Fig 2 Study Areamentioning

confidence: 99%

Housing prices and points of interest in three Polish cities

Cellmer,

Bełej,

Trojanek

2024

J Hous and the Built Environ

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This paper shows the usefulness of open source points-of-interest (POI) data for understanding the causes of volatility in residential property prices. Points of interest are unique locations or features cartographically mapped in space and explicitly connected to various aspects of human life. This point number, and density, primarily reflect a city’s spatial structure. They could therefore indicate a higher quality of life in a given urban zone, resulting in higher demand for housing in the area and, consequently, higher housing prices. This study was conducted in three Polish cities: Warsaw, Poznań and Olsztyn. Our research also attempted to establish which POI categories constitute stimulants and which are destimulants in the housing market. To determine the significance of POI in the price formation process, we used the quantile regression model and its spatial version, apart from classical regression models. The results of our research show the importance of POIs for the formation of housing prices. Individual POI categories are of minor importance, while the simultaneous presence of many different POIs definitely has a positive impact on housing prices.

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“…In the era of big data, the development of the internet and the combination of GIS technology have enabled a large amount of real estate and infrastructure information to be openly and transparently displayed on maps, which establishes a strong data foundation for more detailed and accurate research. Specifically, the spatial analysis function provided by the GIS system can assist researchers in quantifying some location-influencing factors, including using the GIS system's point, line, and surface as well as nuclear density analysis to obtain the distribution of infrastructure and transportation around real estate [43,45,47,100,101]. It also enables an analysis of the impact of important infrastructure on housing prices via buffer analysis [43,102,103] and interpolation analysis to show the spatial distribution of housing prices and so on [99,104].…”

Section: Gis Spatial Analysismentioning

confidence: 99%

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

Wei

Wang

et al. 2022

Land

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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.

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Using Geographic Information and Point of Interest to Estimate Missing Second-Hand Housing Price of Residential Area in Urban Space

Cited by 7 publications

References 11 publications

Points of Interest and Housing Prices

Points of Interest and Housing Prices

Housing prices and points of interest in three Polish cities

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

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