Exploratory Spatial Analysis of Housing Prices Obtained From Web Scraping Technique

Souza, Thaisa; Fonseca, F. D. R.; Fernandes, Vivian de Oliveira; Pedrassoli, Júlio César

doi:10.5194/isprs-archives-xliii-b4-2021-135-2021

Cited by 4 publications

(4 citation statements)

References 27 publications

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“…Esteban Lopez Ochoa  https://orcid.org/0000-0003-1028-3852 Notes 1. This has in turn forced researchers to rely on web scraping techniques to acquire data on housing sale and renting prices (for an example, see Souza, et al, 2021) 2. The central bank price indices include all municipalities (52) of the Metropolitan Region of Santiago, while the empirical application of the Fisher index proposed here only incudes the subset of municipalities (34) that has belong to the urban area of the city of Santiago given that TocToc data is only available in those municipalities.…”

Section: Declaration Of Conflicting Interestsmentioning

confidence: 99%

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Housing price indices from online listing data: Addressing the spatial bias with sampling weights

Lopez

2022

Environment and Planning B: Urban Analytics and City Science

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This paper proposes a method to reduce the inherent sampling bias when estimating housing price indices using online listing data. Producing more accurate and representative metrics is important as new sources of data emerge with higher frequency, detail, and volume, providing more information for policymaking, but usually come with strong sampling biases that are often overlooked. In the case of housing price indices, although the literature around its estimation is abundant, it has concentrated only in traditional and formal sources of housing data, which is normally not available in some markets (i.e. renting) and locations (developing countries). In this paper, I propose a method to create a housing price index (HPI) that is comparable in quality to the industry-standard Case-Shiller HPI but using online listing data. Using online listing data from a developing economy (Chile), this paper shows that large sampling biases present when using raw unweighted data, how these biases can be minimized using sampling weights, and how new and relevant information can be obtained from adjusted HPIs that can lead better policymaking.

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Section: Declaration Of Conflicting Interestsmentioning

confidence: 99%

“…1This has in turn forced researchers to rely on web scraping techniques to acquire data on housing sale and renting prices (for an example, see Souza, et al, 2021)…”

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confidence: 99%

Housing price indices from online listing data: Addressing the spatial bias with sampling weights

Lopez

2022

Environment and Planning B: Urban Analytics and City Science

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“…Some of the studies considered web scrapping technology in order to predict real estate [16]- [19]. Web scraping is a method for obtaining data from the Internet.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Web scraping is a method for obtaining data from the Internet. The process of gathering data from websites and transferring them to a more convenient and more flexible form so that they can be examined and checked with ease is also referred to as web scraping, sometimes web crawling or data scraping, and sometimes data mining or text mining [16].…”

Section: Literature Reviewmentioning

confidence: 99%

Predictive Analysis Using Web Scraping for the Real Estate Market in Gaziantep

ÜZÜMCÜ>

Eligüzel²

2023

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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For investors and people who want to own a property, real estate is a crucial industry. Real estate includes land and any enduring construction, whether natural or artificial, such as houses, residences, apartments, and commercial structures. In Turkey, it is common to believe that owning property makes you live comfortably. Therefore, house ownership is a common aspiration among Turkish families. However, a variety of factors, such as a country's economic structure, inflation, world events, politics, etc., have an impact on the real estate market. In addition, the location, neighborhood, size, and number of rooms of a house can all affect how much it costs to live there. Gaziantep city is considered for analysis in the proposed study. The goal of this study is to predict which neighborhood, given a prospective buyer's financial status and specific property attributes, someone can afford to live in. As a result, web scraping is used to collect real estate data from the website. Once the data has been gathered, forecasting the neighborhood of a house is done using machine learning algorithms including decision trees, random forest, and extra trees. The results demonstrate that all algorithms produce good results with a performance accuracy of over 80%. However, among these algorithms, decision tree classification offers the best performance.

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Computation of High-Frequency Sub-National Spatial Consumer Price Indexes Using Web Scraping Techniques

et al. 2022

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The development of Information and Communications Technology and digital economies has contributed to changes in the consumption of goods and services in various areas of life, affecting the growing expectations of users in relation to price statistics. Therefore, it is important to provide information on differences in consumer prices across space and over time in a timely manner. Web-scraped data, which is the process of collecting large amounts of data from the web, offer the potential to improve greatly the quality and efficiency of consumer price indices. In this paper, we explore the use of web-scraped data for compiling high-frequency price indexes for groups of products by using the time-interaction-region product model. We computed monthly average prices for five entry-level items according to the Consumer Price Index for All Urban Consumers (CPI-U) classification and tracked their evolution over time in 11 USA cities reported in our dataset. Even if our dataset covers a small percentage of the CPI-U index, results show how web scraping data may provide timely estimates of sub-national SPI evolution and unveil seasonal trends for specific categories.

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Exploratory Spatial Analysis of Housing Prices Obtained From Web Scraping Technique

Cited by 4 publications

References 27 publications

Housing price indices from online listing data: Addressing the spatial bias with sampling weights

Housing price indices from online listing data: Addressing the spatial bias with sampling weights

Predictive Analysis Using Web Scraping for the Real Estate Market in Gaziantep

Computation of High-Frequency Sub-National Spatial Consumer Price Indexes Using Web Scraping Techniques

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