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Journal articles on the topic 'Housing data'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

B. Sanjai Prasada Rao et al. "Smart Data Analysis (Housing Data)." Proceeding International Conference on Science and Engineering 11, no. 1 (2023): 98–105. http://dx.doi.org/10.52783/cienceng.v11i1.98.

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In the modern era, the immense value which data holds goes into a waste as much of this information is not prominently visible to the common user, which the internet hosts. One of such areas is Renting/Buying houses in India. Today many people are still relying on brokers who are dishonest for buying/renting their homes. Even though many websites host good amount of information for the same purpose, many go unnoticed. This project aims to simplify the process of collecting and understanding the hosing data to use it effectively. The first module of the project deals with web scraping which all

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Anderson, Courtney Lauren. "Data Sharing to Combat Segregation." Journal of Law, Medicine & Ethics 50, no. 4 (2022): 769–75. http://dx.doi.org/10.1017/jme.2023.18.

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AbstractData sharing between housing and education agencies will provide housing agencies with resources to assist them with efforts to decrease segregation and mitigate the adverse health outcomes experienced by people of color. The Fair Housing Act has the potential to fulfill its original integrationist purpose if housing and education agencies combine resources and data to create and implement fair housing plans. The Biden Administration’s restored rule to affirmatively further fair housing pursuant to the Fair Housing Act of 1968 which seeks to reduce segregation and increase housing equi

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Susilo, B., and A. Cahyono. "Utilization of online geospatial data sources for oikonym study: mapping and analysis of housing name in capital area of kulon progo regency." IOP Conference Series: Earth and Environmental Science 1089, no. 1 (2022): 012030. http://dx.doi.org/10.1088/1755-1315/1089/1/012030.

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Abstract Oikonym is a part of toponym that focus the study on the name given to inhabited place. In the past, availability of data was one of the obstacles in the study of toponym as well as oikonym. Now days, the development of digital mapping and information technology particularly internet enables oikonym data obtained from a variety of sources. This study aimed to explore the typology of housing names as well as the geographical characteristic of the housing location by means of mapping and spatial analysis. Data required for mapping and analysis were obtained via internet therefore referr

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Chen, Meifang, Yongwan Chun, and Daniel A. Griffith. "Delineating Housing Submarkets Using Space–Time House Sales Data: Spatially Constrained Data-Driven Approaches." Journal of Risk and Financial Management 16, no. 6 (2023): 291. http://dx.doi.org/10.3390/jrfm16060291.

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With the increasing availability of large volumes of space–time house data, delineating space–time housing submarkets is of interest to real estate agents, homebuyers, urban policymakers, and spatial researchers, among others. Appropriately delineated housing submarkets can help nurture submarket monitoring and housing policy developments. Although submarkets are often expected to represent areas with similar houses, neighborhoods, and amenities characteristics, delineating spatially contiguous areas with virtually no fragmented small areas remains challenging. Furthermore, housing submarkets

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Nelson, Kathryn P. "Housing Assistance Needs and the Housing Stock: Data for Comprehensive Housing Affordability Strategies." Journal of the American Planning Association 58, no. 1 (1992): 85–102. http://dx.doi.org/10.1080/01944369208975538.

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Tong, Yuxuan. "Housing data visualization and analysis." Applied and Computational Engineering 69, no. 1 (2024): 154–61. http://dx.doi.org/10.54254/2755-2721/69/20241518.

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Data visualization is a powerful tool that can assist individuals and organisations in comprehending vast amounts of data and extracting valuable insights from it. The most significant function of data visualization is to make recommendations by figuring out the essence of the occurrence of the data. This paper will take housing data as an example, raise relevant questions, and reveal the logic behind the data and the relationship between variables through data visualization, linear regression, and other statistical methods.

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Clair, Amy, and Amanda Hughes. "Housing and health: new evidence using biomarker data." Journal of Epidemiology and Community Health 73, no. 3 (2019): 256–62. http://dx.doi.org/10.1136/jech-2018-211431.

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BackgroundThe link between housing and health is well established and long-standing, however much of the evidence relies on self-reported health measures. While these are useful, the availability of biomarker data allows us to add to this evidence using objective indicators of health.MethodsIn this paper, we use C-reactive protein (CRP), a biomarker associated with infection and stress, alongside information relating to housing details, demographic characteristics and health behaviours taken from the UK Household Longitudinal Study. Hierarchical linear regression models estimate CRP for indivi

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Siahaan, Elisabet, Khaira Amalia Fachrudin, Magdalena Linda Leonita Sibarani, and Iskandar Muda. "Evaluating customer perceived value of housing based on location factor and economic value." Problems and Perspectives in Management 17, no. 3 (2019): 196–206. http://dx.doi.org/10.21511/ppm.17(3).2019.16.

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This study seeks to evaluate customer perceived value in housing in order to increase the construction of housing value from the consumer’s point of view. A total of 240 houseowners in the middle to upper housings in Medan city, Indonesia, participated in this study. The data analysis used in this study is the Partial Least Square (PLS) approach. The results indicate that the perception of consumer value is strongly influenced by the economic value of the housing and the location function of the housing. Housing offered should be able to meet the needs of consumers and has the good mobility an

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Aranski, Alvendo Wahyu, Sarah Astiti, Riko Andrian Putra, and Darmansah Darmansah. "Pengaplikasian Data Mining Dalam Mengelompokan Data Penerima Bantuan Subsidi Rumah dengan Menggunakan Metode K-Means Clustering." Building of Informatics, Technology and Science (BITS) 6, no. 1 (2024): 480–89. https://doi.org/10.47065/bits.v6i1.5366.

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From 2015 until now, the government has provided assistance for house renovations in the Tembesi area, Sagulung sub-district, Batam City. However, in determining the provision of assistance, sub-district governments sometimes face problems in determining which people will receive housing subsidies and there is no scheme or category for determining recipients of assistance. Therefore, the author will conduct this research by grouping or clustering the eligibility of recipients of housing subsidy assistance using the K-Means algorithm. The K-Means clustering algorithm can group each data into se

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Jiang, Xufeng, Zelu Jia, Lefei Li, and Tianhong Zhao. "Understanding Housing Prices Using Geographic Big Data: A Case Study in Shenzhen." Sustainability 14, no. 9 (2022): 5307. http://dx.doi.org/10.3390/su14095307.

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Understanding the spatial pattern of urban house prices and its association with the built environment is of great significance to housing policymaking and urban planning. However, many studies on the influencing factors of urban housing prices conduct qualitative analyses using statistical data and manual survey data. In addition, traditional housing price models are mostly linear models that cannot explain the distribution of housing prices in urban areas. In this paper, we propose using geographic big data and zonal nonlinear feature machine learning models to understand housing prices. Fir

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Helbich, Marco, Wolfgang Brunauer, Julian Hagenauer, and Michael Leitner. "Data-Driven Regionalization of Housing Markets." Annals of the Association of American Geographers 103, no. 4 (2013): 871–89. http://dx.doi.org/10.1080/00045608.2012.707587.

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Börsch-Supan, Axel. "Panel data analysis of housing choices." Regional Science and Urban Economics 20, no. 1 (1990): 65–82. http://dx.doi.org/10.1016/0166-0462(90)90025-x.

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Laurent, Amy A., Alastair Matheson, Katie Escudero, and Andria Lazaga. "Linking Health and Housing Data to Create a Sustainable Cross-Sector Partnership." American Journal of Public Health 110, S2 (2020): S222—S224. http://dx.doi.org/10.2105/ajph.2020.305693.

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In response to the growing regional (and national) focus on health and housing intersections, two public housing authorities (PHAs) in Washington—the King County Housing Authority and the Seattle Housing Authority—joined with Public Health–Seattle & King County to form the Housing and Health (H&H) partnership in 2016. H&H linked Medicaid health claims with PHA administrative data to create a sustainable public-facing dashboard that informs health and housing stakeholders such as an Accountable Community of Health (a governing body that oversees local Medicaid transformation project

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Kwon, Hyuk Jin, and Mi Ho Choi. "Comparative Analysis of the Effects of Rental Housing Supply Provider and Method on the Housing Market Price Stabilization." Korea Real Estate Institute 34, no. 1 (2024): 7–22. http://dx.doi.org/10.35136/krer.34.1.1.

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This study conducted a panel analysis to examine the impact of rental housing supply by type (public/private, built/purchased) on housing market price stabilization at the level of metropolitan city and province level. This study used data from the National Survey of House Price Trends (Korea Real Estate Board) and MOLIT Statistics System (Ministry of Land, Infrastructure, and Transport) for the period 2013–2021. The main findings of the analysis are as follows. In terms of a provider, publicly rented housings contribute more to price stabilization than privately rented, and in terms of a supp

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Li, Ming, Guojun Zhang, Yunliang Chen, and Chunshan Zhou. "Evaluation of Residential Housing Prices on the Internet: Data Pitfalls." Complexity 2019 (February 19, 2019): 1–15. http://dx.doi.org/10.1155/2019/5370961.

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Many studies have used housing prices on the Internet real estate information platforms as data sources, but platforms differ in the nature and quality of the data they release. However, few studies have analysed these differences or their effect on research. In this study, second-hand neighbourhood housing prices and information on five online real estate information platforms in Guangzhou, China, were comparatively analysed and the performance of neighbourhoods’ raw information from four for-profit online real estate information platforms was evaluated by applying the same housing price mode

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OBISANYA, Abiodun Richard, Olamide Titilayo AYODEJI, and Emmanuel Olatubosun SOWUNMI. "Government Finance and Housing Delivery of Selected Local Governments in Ogun State, Nigeria." Lead City Journal of the Social Sciences (LCJSS) Volume 8, Issue 2, July, 2023 (2023): 69–87. https://doi.org/10.5281/zenodo.10401995.

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The problem of housing globally became aggravated after the scourge of Covid 19 in 2020. Mainlyaffected are the increasing low-income earners in developing countries struggling with over populationwithout a corresponding housing supply and stable mortgage programme. This made it imperative tostudy the phenomenon of housing deficit in Ogun State. The objective of this study is to look at the nexusbetween housing deficit vis a vis housing delivery and government finance in Sagamu, Ijebu Ode, AdoOdo Ota and Abeokuta south local governments in Ogun State. The study adopted the exploratoryresearch

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Sreelatha, Gavini, Veeresh Dachepalli, and Gudur Sahiti. "DATA ANALYSIS FOR STUDENTS BASED ON GEOLOCATION APPROACH." International Journal of Interpreting Enigma Engineers 01, no. 03 (2024): 33–41. http://dx.doi.org/10.62674/ijiee.2024.v1i03.005.

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It is quite important for students, particularly those who are new to strange places, to find appropriate housing. Some viable solutions to this difficulty can be found in advanced analytical approaches such as geolocational analysis and clustering algorithms. The optimal student housing options in any city can be found by thoroughly exploring geolocational data, as demonstrated in this study. In order to organise housing possibilities according to criteria such as budget, proximity to amenities, and availability, we employ the popular K-Means Clustering technique. Our objective is to provide

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Jeong, Ui-Chung, and Eui-Chul Chung. "A Panel Data Analysis of Housing Tenure Choice Decision of Millennials." Konkuk Research Institute of Real Estate and Urban Studies 15, no. 1 (2022): 49–68. http://dx.doi.org/10.22423/kreus.2022.15.1.49.

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This study examines the determinants of housing tenure choice decisions of Millennials. Using KLIPS panel data from 2014 to 2020, a correlated random effects probit model and a fixed effects logit model were estimated to control for endogeneity problem associated with time-invariant unobserved heterogeneity. Estimation results showed that net wealth, relative housing cost, marital status of household heads were major determinants of housing tenure choice decisions. Permanent income, one of key variables on the housing tenure choice, was not significant, indicating that net wealth rather

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Eriksson, Erik, Steven Schmidt, and Maya Kylén. "Reasoning About Life Transitions in Relation to Perceived Housing: In-Depth Data From Sweden." Innovation in Aging 5, Supplement_1 (2021): 442–43. http://dx.doi.org/10.1093/geroni/igab046.1717.

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Abstract The accessibility and suitability of housing to improve health and well-being for the growing share of older adults in the population has important policy implications. Yet, current housing policies tend to neglect the heterogeneity of older adults housing needs which vary across age, health, and personal preferences. Little is known about how life transitions in combination with perceived housing relates to good aging. This qualitative study aims to explore the relationship between perceived housing, life transitions, well-being, health, and participation in older age. Participants w

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Oyetunji, Tosin Samuel, Fasasi Lanre Erinjogunola, Rasheed O. Ajirotutu, Abiodun Benedict Adeyemi, Tochi Chimaobi Ohakawa, and Saliu Alani Adio. "Smart Data-Driven Analysis of Affordable Housing Crisis Impact on Underserved Communities." International Journal of Multidisciplinary Research and Growth Evaluation 5, no. 1 (2024): 1617–25. https://doi.org/10.54660/.ijmrge.2024.5.1.1617-1625.

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The affordable housing crisis has become a critical socio-economic challenge, disproportionately affecting underserved communities and exacerbating inequalities. Outdated data, reactive strategies, and inefficiencies in resource allocation have often hindered traditional policy interventions. This paper explores the role of smart data-driven analysis in addressing housing affordability challenges through predictive analytics, big data integration, and artificial intelligence. By leveraging large-scale datasets, including census data, rental market trends, and socioeconomic indicators, data-dri

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Bi, Shuoben, Luye Wang, Shaoli Liu, Lili Zhang, and Cong Yuan. "Identification of Urban Jobs–Housing Sites Based on Online Car-Hailing Data." Sustainability 15, no. 2 (2023): 1712. http://dx.doi.org/10.3390/su15021712.

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With the development of cities, the organization of jobs–housing space is becoming more complex, and the rapid, effective identification of both residences and workplaces is crucial to sustainable urban development. The long time series of online car-hailing data conveys a large amount of activity trajectory information about urban populations, which can represent the social functions of urban areas, including workplaces and residences. This paper constructs a jobs–housing site identification model based on human activity characteristics. This model uses a time series dataset of online car hai

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Kamruzzaman, Liton (Md), Sanaz Nikfalazar, Fuad Yasin Huda, Dharmalingam Arunachalam, and Dickson Lukose. "Data Typologies in Urban Housing Research: A Systematic Review of the Literature." Sustainability 17, no. 11 (2025): 4809. https://doi.org/10.3390/su17114809.

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The increasing digitalisation of housing markets has expanded the types and sources of data available for research. However, there is limited understanding of how these diverse data types are used across different themes in urban housing studies and which analytical approaches are applied. This study addresses these questions through a systematic review of 71 peer-reviewed studies published between 2010 and 2021, following PRISMA guidelines. The review identifies five dominant research themes: housing market analysis, rental market analysis, housing policy evaluation, housing affordability, an

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Manaf, Asnawi, and Mirza Irwansyah. "Towards sustainable built environment: learning from socio-economic characteristics, housing norms and satisfactions of three settlement patterns in Semarang City, Indonesia." IOP Conference Series: Earth and Environmental Science 1394, no. 1 (2024): 012002. http://dx.doi.org/10.1088/1755-1315/1394/1/012002.

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Abstract his article presents the results of a comparative study of socio-economic characteristics, housing norms, and satisfaction of low-income families living in three different settlement patterns as a perspective toward the sustainable built environment in Semarang City, Indonesia. The research used the survey method to examine households living in Kaligawe low-cost rental flats, informal settlements, and landed houses near the Kaligawe flats. Data were obtained by distributing questionnaires to 97 respondents selected using a simple random sampling technique. The findings indicated that

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Sairi, Nur Asyikin Mohd, Burhaida Burhan, and Edie Ezwan Mohd Safian. "Identifying Housing Submarkets in Johor Bahru and Kulai, Malaysia: A Data-Driven Method." Real Estate Management and Valuation 30, no. 2 (2022): 1–11. http://dx.doi.org/10.2478/remav-2022-0009.

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Abstract Generally, housing within the same area tends to share similar structural and locational characteristics as well as being characterized by the same homebuyers’ preferences. This information provides insight on the existence of housing submarkets. Thus, this study aims to identify the existence of housing submarkets in Johor Bahru and Kulai, Malaysia through principal component analysis and cluster analysis. A total of 29 housing attributes were included in the study. The results from the principal component analysis have successfully identified four principal components that represent

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Kim, Ki Pyung, and Kenneth Sungho Park. "Housing information modelling for BIM-embedded housing refurbishment." Journal of Facilities Management 16, no. 3 (2018): 299–314. http://dx.doi.org/10.1108/jfm-09-2016-0037.

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Purpose The purpose of this study is to identify building information modelling (BIM) input data sets within a BIM-embedded housing refurbishment process and enable construction professionals to use BIM as an information management platform for housing refurbishment projects. Design/methodology/approach A hypothetical case study using BIM tools for a housing refurbishment project is adopted to identify BIM input data sets to create a housing information model within a BIM system. Reliability of the research outcome is examined by conducting a comparative analysis between existing and simulated

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Chica-Olmo, Jorge, Rafael Cano-Guervos, and Mario Chica-Rivas. "Estimation of Housing Price Variations Using Spatio-Temporal Data." Sustainability 11, no. 6 (2019): 1551. http://dx.doi.org/10.3390/su11061551.

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This paper proposes a hedonic regression model to estimate housing prices and the spatial variability of prices over multiple years. Using the model, maps are obtained that represent areas of the city where there have been positive or negative changes in housing prices. The regression-cokriging (RCK) method is used to predict housing prices. The results are compared to the cokriging with external drift (CKED) model, also known as universal cokriging (UCK). To apply the model, heterotopic data of homes for sale at different moments in time are used. The procedure is applied to predict the spati

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Dou, Yuxin. "Utilizing 31 Chinese province panel data models to investigate the factors influencing house prices." Theoretical and Natural Science 39, no. 1 (2024): 121–28. http://dx.doi.org/10.54254/2753-8818/39/20240584.

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In the realm of data analysis, this research employs regression models to delve into the complexities of housing market dynamics. The construction sector of real estate development companies, the amount invested in real estate development, and the gross regional product have all been found to be important determinants of home prices. Interestingly, the most significant factor is the investment in real estate development, which has a significant impact on house prices. The analysis reveals a positive correlation between the gross regional product and investment amount in real estate development

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Benzouai, M. C. "Measuring the Effect of Rural Housing Support on Agricultural Activity: A Panel-Data Analysis for Ain M'lila Province." Inter. J. Res. Methodol. Soc. Sci 2, no. 3 (2016): 6–16. https://doi.org/10.5281/zenodo.1321567.

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The goal of this study is to measure the effect of rural housing support policy on agricultural activity through a Dynamic Panel Data Model upon yearly data for the period 2004-2014. Results show that a co-integration relationship is detected between rural housing financial support amounts and agricultural activity in Ain M'lila province at the long run. The positive impact emphasizes the success of the rural housing support policy in Algeria.

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Kim, BoRin, Sojung Park, Casey Golomsky, et al. "Multilevel Factors for Life Satisfaction Among Residents in Non-Urban Subsidized Senior Housing." Innovation in Aging 4, Supplement_1 (2020): 109. http://dx.doi.org/10.1093/geroni/igaa057.360.

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Abstract Although a quarter of HUD-assisted properties for older adults are located in rural/non-metropolitan areas, there is limited understanding of the population living in these locations. Advanced age and low income are known risk factors for poor physical and mental health. Older adults in rural subsidized housings may be at increased risk for poor health and social isolation due to their isolated locations and small-scale housing complexes. This presents the additional challenge of service provision for the residents’ needs. This study aims to explore multi-level factors affecting life

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TONOOKA, Yutaka. "Housing LCA and Environmental Load Data Base." Journal of Life Cycle Assessment, Japan 13, no. 2 (2017): 126–33. http://dx.doi.org/10.3370/lca.13.126.

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Goodman, John. "Using Attitude Data to Forecast Housing Activity." Journal of Real Estate Research 9, no. 4 (1994): 445–53. http://dx.doi.org/10.1080/10835547.1994.12090762.

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Phillips, David C. "Measuring Housing Stability With Consumer Reference Data." Demography 57, no. 4 (2020): 1323–44. http://dx.doi.org/10.1007/s13524-020-00893-5.

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Börsch-Supan, Axel, and Henry O. Pollakowski. "Estimating housing consumption adjustments from panel data." Journal of Urban Economics 27, no. 2 (1990): 131–50. http://dx.doi.org/10.1016/0094-1190(90)90011-b.

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van Otterloo, Sieuwert, and Pavlo Burda. "The Utrecht Housing dataset: A housing appraisal dataset." Computers and Society Research Journal 1 (2025): 1–11. https://doi.org/10.54822/qvhm1662.

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This paper introduces a real-world dataset for analysing and predicting house prices. The dataset consists of actual data on the Dutch housing market collected in 2024 for a total of 153 houses in one city (Utrecht in The Netherlands). The dataset incorporates diverse variables on individual houses, includ- ing property characteristics (e.g., house type, build year, geolocation, area, energy label) and market metrics (e.g., asking price, final price). The data was collected from two public sources. The dataset has been created to help researchers and educators to demonstrate machine learning p

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Hou, C. H. Y., and J. H. K. Lai. "User-centric Student Housing Performance Evaluation Approach: A Case Study in the Netherlands." IOP Conference Series: Earth and Environmental Science 1101, no. 6 (2022): 062015. http://dx.doi.org/10.1088/1755-1315/1101/6/062015.

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Abstract In the Netherlands, the development of student housing has been increasing with the significant growth of the student recruitment in recent years. As such, there is a pressing need for a retrospective reflection of the performance of student housing built in previous years. However, a user-centric building performance evaluation framework (BPEF) for student housing is not available. From the user perspective, this study aims to develop a BPEF to support student housing management. Based on a thorough literature review, 14 building performance attributes (BPAs) applicable to student ho

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Frank Nwaogelenya Opia, Kathleen Peterson–Sgro, Ozioma Jerry Gabriel, et al. "Housing instability and mental health among low-income minorities: Insights from Illinois BRFSS data." World Journal of Advanced Research and Reviews 25, no. 1 (2025): 2391–401. https://doi.org/10.30574/wjarr.2025.25.1.0213.

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Housing instability, characterized by homelessness, overcrowding, frequent moves, and financial strain, poses significant challenges to mental health, especially among low-income minority populations. This review investigates the relationship between housing instability and mental health outcomes in Illinois, focusing on systemic inequities affecting marginalized communities. Using 2020–2022 Behavioral Risk Factor Surveillance System (BRFSS) data, a cross-sectional analysis was conducted to evaluate the prevalence and impact of housing instability on mental health, with additional qualitative

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Dalton, Craig M. "Rhizomatic data assemblages: mapping new possibilities for urban housing data." Urban Geography 41, no. 8 (2019): 1090–108. http://dx.doi.org/10.1080/02723638.2019.1645553.

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Li, Junjie, Yu Wang, and Chunlu Liu. "SPATIAL EFFECT OF MARKET SENTIMENT ON HOUSING PRICE: EVIDENCE FROM SOCIAL MEDIA DATA IN CHINA." International Journal of Strategic Property Management 26, no. 1 (2022): 72–85. http://dx.doi.org/10.3846/ijspm.2022.16255.

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Market sentiment has become more easily spread between cities through social media. This study investigates the spatial effect of market sentiment on housing price in a social media environment. In order to extract home-buyer sentiment from social media, we use text sentiment analysis techniques and build a novel housing market sentiment index. A spatial econometric model of housing price volatility is subsequently constructed and the housing market sentiment index is included as an independent variable in the model. Using panel data from 30 large and medium-sized cities in China for 20 quarte

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Tandoh, Francis, and Devi Datt Tewari. "The income and price elasticity of demand for housing in Ghana: Empirical evidence from household level data." South African Journal of Economic and Management Sciences 19, no. 2 (2016): 160–74. http://dx.doi.org/10.4102/sajems.v19i2.675.

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Housing is a challenging issue in Ghana, due to the rising demand and sluggish supply which has led to a deficit of more than two million housing units. This study aimed to estimate and analyse the determinants of the demand for housing in Ghana. The estimated elasticities show that owner and rental demand for housing is price and income inelastic. Permanent income elasticities were greater in each case than current income elasticity. The quantile regression showed that permanent income, current income and price were significant for all quantiles of housing units consumed. It is recommended th

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Deng, Guangjun, Weihan Zhou, and Dingxing Wang. "Has China’s Housing Security Policy Affected the Housing Market?—Analysis Based on Housing Market Data from 35 Monitored Cities." Buildings 15, no. 11 (2025): 1847. https://doi.org/10.3390/buildings15111847.

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This study investigates how China’s affordable housing policies have shaped the real estate market, using data from 35 major cities between 2010 and 2023. By analyzing housing prices, sales, and investment trends with advanced statistical methods, we found that increasing the supply of affordable housing significantly slows down rising home prices, especially in cities with high housing costs. During the COVID-19 pandemic, these policies also helped stabilize the market by boosting housing sales and reducing price volatility. Our research highlights regional differences: affordable housing wor

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Afanasev, Vladislav V., and Yulia A. Tarasova. "Default Prediction for Housing and Utilities Management Firms Using Non-Financial Data." Financial Journal 14, no. 6 (2022): 91–110. http://dx.doi.org/10.31107/2075-1990-2022-6-91-110.

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For many years, financial ratios have been used as predictors of default. However, biases in financial statements of companies in Russia call into question the applicability of this approach. An alternative approach is to use non-financial data in such models. The purpose of this paper is to find out whether non-financial data, such as information related to court trials, unscheduled inspections and firm age, can significantly improve the accuracy of default prediction in the housing and utilities management industry. This part of the services sector is chosen as one of the riskiest industries

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Lian, Yukun, Shuyuan Li, and Haizhen Wen. "Conceptual Housing and Housing Prices: An Empirical Study Based on Micro-big Data of Hangzhou Newly-built Housing Market." Journal of Physics: Conference Series 1616 (August 2020): 012019. http://dx.doi.org/10.1088/1742-6596/1616/1/012019.

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Tran, Anh-Ton, Ashley Boone, Christopher A. Le Dantec, and Carl DiSalvo. "Careful Data Tinkering." Proceedings of the ACM on Human-Computer Interaction 6, CSCW2 (2022): 1–29. http://dx.doi.org/10.1145/3555532.

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In this paper, we describe and analyze the data practices of an activist non-profit, the Housing Justice League. We focus on their Tenant Power Hotline, a community outreach tool for tenants facing eviction and seeking organizing support. This research contributes to existing scholarship that examines data practices and the use of information and communication technologies in non-profit and grassroots organizations. To this existing scholarship, we share the structure of a counter-institution: an organization that strives to operate outside the non-profit industrial complex. We then interpret

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Huang, Jie, David Levinson, Jiaoe Wang, Jiangping Zhou, and Zi-jia Wang. "Tracking job and housing dynamics with smartcard data." Proceedings of the National Academy of Sciences 115, no. 50 (2018): 12710–15. http://dx.doi.org/10.1073/pnas.1815928115.

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Residential locations, the jobs–housing relationship, and commuting patterns are key elements to understand urban spatial structure and how city dwellers live. Their successive interaction is important for various fields including urban planning, transport, intraurban migration studies, and social science. However, understanding of the long-term trajectories of workplace and home location, and the resulting commuting patterns, is still limited due to lack of year-to-year data tracking individual behavior. With a 7-y transit smartcard dataset, this paper traces individual trajectories of reside

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Lan, Feng, Linlin Yan, Tao Zhou, Yiming Wang, and Chengcai Jiao. "HOUSING DIFFERENCES, CREDIT CONSTRAINTS, AND HOUSEHOLD ENERGY CONSUMPTION: AN EMPIRICAL STUDY FROM HOUSEHOLD TRACKING SURVEY DATA IN CHINA." Journal of Green Building 18, no. 1 (2023): 71–93. http://dx.doi.org/10.3992/jgb.18.1.71.

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ABSTRACT Although many studies have shown that housing wealth impacts household consumption behavior patterns, they have not incorporated housing characteristics into their research framework. Against the background of China’s reformed housing system, this study therefore constructs a theoretical framework of housing differences, credit constraints, and household energy consumption by adopting sample data from China Family Panel Studies (CFPS) of 7064 households in 25 provinces, municipalities, and autonomous regions in China. Multiple regression methodology is used based on the emission peak

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Zhou, Chucheng. "Prediction of second-hand house prices in Ames city based on data mining." Applied and Computational Engineering 46, no. 1 (2024): 159–70. http://dx.doi.org/10.54254/2755-2721/46/20241300.

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As the secondary housing market continues to play a vital role in the global real estate industry, there is an increasing demand for accurate and efficient prediction of secondary housing prices. In this study, transaction data from the second-hand housing market in Ames, Iowa, USA, available on the Kaggle website, was utilized. A total of 79 influential feature variables, including Lot Area, Overall Quality, and Year Built, were selected to construct seven different models for predicting secondary housing prices: Linear Regression, Lasso Regression, Ridge Regression, LassoCV, RidgeCV, Random

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AKINYEMI, Akinniyi Tunde, Hezekiah Adedayo AYOOLA, and Abraham Adeniyi TAIWO. "An Appraisal of Housing Policies Performances in Nigeria: Associated Problems and Past Efforts of Government." Euro Afro Studies International Journal (EASIJ) 4, no. 8 (2022): 155–67. https://doi.org/10.5281/zenodo.7162151.

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Housing, like food and clothing, is a basic human requirement. A person's well-being, survival, and health depend on it. This study appraised housing policies performances in Nigeria, with examination of associated problems and past efforts of government. Content analysis and assessment techniques were utilized to gather and analyze credible and valid data on Nigerian housing policy concerns and housing condition outcomes in this expository research. Nigeria's housing policies and problems were analyzed using books, existing records, published works in the form of yearly reports and re

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Maulida, Lydia, and Yohanna Magdalena Lydia Gultom. "Do the Relocated Residents Differ from Public Residents in Rent Overdue? The Case of DKI Jakarta Public Rental Housing." Jurnal Perencanaan Pembangunan: The Indonesian Journal of Development Planning 7, no. 1 (2023): 180–91. http://dx.doi.org/10.36574/jpp.v7i1.432.

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Housing problems in urban areas are very critical. The increased population growth in DKI Jakarta and the high migration rate from rural to urban areas cause slums. Slums are generated because many households do not have place to live and populate in unauthorized and inappropriate regions. More than 50% of households in DKI Jakarta have yet to own any housing property. In addition, the housing backlog in DKI Jakarta reached 302.319 in 2017. Therefore, housing problems in urban areas are very critical. One way to overcome this issue is to provide a public rental housing program for relocated an

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Rahayu, Ardiani Putri, Sharilla Aryananti Abidin, Afifah Faradilla Islami, M. Syafiq Hazlan Ruslan, and Kiki Adi Kurnia. "A Study on The Healthy Rental-Housing for College Students According to World Health Organization: Evidence from Surabaya City in Indonesia." Journal of Public Health for Tropical and Coastal Region 4, no. 3 (2021): 125–31. http://dx.doi.org/10.14710/jphtcr.v4i3.10816.

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Introduction:Many college students live far from their parents to study to get a better higher education, so they occupy a rental-housing. However,many rental-housings do not meet the health standards according to the World Health Organization (WHO). Therefore, this study aiming at evaluating the rental-housing quality used by college students in Surabaya, Jawa Timur, Indonesia.Methods:This type of research is an observational study conducted with a descriptive observational design. The sample in this study was 52 college students who originated from outside Surabaya and currently living in th

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Chen, Patricia M. "Housing First and Single-Site Housing." Social Sciences 8, no. 4 (2019): 129. http://dx.doi.org/10.3390/socsci8040129.

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In 2002, the United States embraced the Housing First approach, which led to the widespread adoption of this approach in cities across the nation. This resulted in programmatic variations of Housing First and calls for clarity about the Housing First model. This study uses a comparative case study approach to explore the differences across Housing First programs in five selected cities: Dallas, Austin, Houston, Los Angeles, and Salt Lake City. It focuses on one aspect of programmatic variation: housing type. Data collection consisted of in-depth interviews with 53 participants, documentation r

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