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Relevant bibliographies by topics / Housing data

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Academic literature on the topic 'Housing data'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Housing data"

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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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Dissertations / Theses on the topic "Housing data"

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Morgan, Malcolm Edward. "Sustainability assessment of England's housing using open data." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709279.

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Kim, Myung Woon. "Family structure, residential area and housing demand evidence from micro-data for the U.S. /." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/4785.

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Thesis (Ph. D.)--University of Missouri-Columbia, 2007.<br>The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on September 26, 2007) Vita. Includes bibliographical references.

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Chen, Suifeng. "The role of data in housing design in China." Click to view the E-thesis via HKUTO, 2004. http://sunzi.lib.hku.hk/hkuto/record/B43895608.

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Chen, Suifeng, and 陳穗峰. "The role of data in housing design in China." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B43895608.

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Lee, Nai Jia. "Panel data analyses of urban economics and housing markets." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/55133.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 2009.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 96-100).<br>The thesis looks three pertinent issues in Housing Market and Urban Economics literature with panel data- home sales and house price relationship, efficiency of housing market and commercial property taxation. For the first part, I examine the strong positive correlation that exists between the volume of housing sales and housing prices. I develop a simple model of these flows which suggests they gener

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Allard, Nathan, and Tobias Hagström. "Modern Housing Valuation : A Machine Learning Approach." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301866.

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The primary goal of this report was to examine and demonstrate how machine learning methods can be used to produce accurate and useful apartment valuation models, focusing on the Stockholm market. Furthermore, the paper analyzed how different attributes, including apartment descriptions, affect the price of an apartment. Accurate and efficient valuation could not only be useful for individual property owners, who want a quick and precise valuation, but also for real estate agencies and tax authorities. To this end, several different methods were applied and evaluated, the best of which achieve

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Cheng, Man-fai, and 鄭文輝. "Utitlization in information technology in housing management." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B4500920X.

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Shen, Jr-Yi. "Vibration of rotating disk/spindle systems with flexible housing/stator assemblies /." Thesis, Connect to this title online; UW restricted, 2002. http://hdl.handle.net/1773/7059.

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Liu, Nan. "Measures of Affordable Housing and Applications on Beijing Data 2004-2013." Thesis, KTH, Fastigheter och byggande, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-183416.

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With the change in political and economic situations, increased urbanization and the growing population in China leads to extended housing needs, which results in various problems for the real estate market, such as the pronounced rise of housing price and unreasonable housing supply system. At present, the major issue that the government is facing with is how to improve the housing availability and provide space to the urban residents to solve the problem that many urban residents are unable to afford housing. In order to fully understand the housing market in Beijing city, this paper adopted

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Sax, Kaijser Per. "Tobin’s Q theory and regional housing investment : Empirical analysis on Swedish data." Thesis, Uppsala universitet, Nationalekonomiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-226661.

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This thesis investigates the relationship between Tobin’s Q and regional housing investment in Sweden for the time period of 1998-2012. The relationship is tested through estimation of two models for time-series analysis, a vector error correction model (VECM) and an autoregressive distributed lag (ARDL) model. Depending on which model that is used, I find some evidence of positive correlation between Tobin’s Q and regional housing investment in the long run while the short run dynamics of investment does not seem to be explained by Tobin’s Q. By transforming the regional data into a panel dat

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Books on the topic "Housing data"

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G, Brown Mark, and Rhode Island. Dept. of Administration. Division of Planning., eds. Housing data base. Division of Planning, Rhode Island Dept. of Administration, 1996.

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G, Brown Mark, and Rhode Island Statewide Planning Program., eds. Housing data base. Statewide Planning Program, Dept. of Administration, 2003.

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Rhode Island. Dept. of Administration. Division of Planning., ed. Housing data base. Division of Planning, Rhode Island Dept. of Administration, 1990.

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Rhode Island. Dept. of Administration. Division of Planning., ed. Housing data base. Division of Planning, Rhode Island Dept. of Administration, 1992.

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United States. Bureau of Labor Statistics, ed. Housing data collection manual. U.S. Department of Labor, Bureau of Labor Statistics, 2004.

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United States. Dept. of Housing and Urban Development. Office of Policy Development and Research. and United States. Bureau of the Census., eds. The American Housing Survey: Housing data between the censuses. Dept. of Housing and Urban Development, Office of Policy Development and Research, 1992.

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United States. Dept. of Housing and Urban Development. Office of Policy Development and Research and United States. Bureau of the Census, eds. The American Housing Survey: Housing data between the censuses. Dept. of Housing and Urban Development, Office of Policy Development and Research, 1992.

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United States. Dept. of Housing and Urban Development. Office of Policy Development and Research. and United States. Bureau of the Census., eds. The American Housing Survey: Housing data between the censuses. Dept. of Housing and Urban Development, Office of Policy Development and Research, 1992.

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United States. Bureau of the Census, ed. The American Housing Survey: Housing data between the censuses. Department of Housing and Urban Development, Office of Policy Development and Research, 1992.

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Peter, Kemp. Housing benefit data and literature sourcebook. Social Research Branch, Analytical Services Division, Dept. of Social Security, 1998.

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Book chapters on the topic "Housing data"

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Ramasubramanian, Laxmi, Jochen Albrecht, and Deborah Rojas De Leon. "Data for Housing Research." In GIS and Housing. CRC Press, 2023. http://dx.doi.org/10.1201/9780429243066-4.

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Lindgren, David T. "Population and housing data." In Land use planning and remote sensing. Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-017-2035-9_11.

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Kürschner, Kathleen. "Empirical Strategy and Data." In Immigration and Housing Rents. Springer Fachmedien Wiesbaden, 2015. http://dx.doi.org/10.1007/978-3-658-12061-0_3.

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Meen, Geoffrey. "Issues in Housing Data Analysis." In Modelling Spatial Housing Markets. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1673-6_2.

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Souza, Lawrence A., Hannah Macsata, Dustin Hartuv, Joshua Martinez, and Alicia Bilbrey-Becker. "Data Analysis Plan." In U.S. Housing Policy, Politics, and Economics. Routledge, 2021. http://dx.doi.org/10.4324/9781003223436-7.

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Shibuya, Yuya. "The Excess Demand for Housing." In Social Media Communication Data for Recovery. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0825-7_5.

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Holian, Matthew J. "At Home: Housing and Energy Use." In Data and the American Dream. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-64262-4_2.

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Sarıoğlu Erdoğdu, G. Pelin. "Limitations of Housing Research Data in Turkey and Proposals for a Better System." In Housing in Turkey. Routledge, 2022. http://dx.doi.org/10.4324/9781003173670-6.

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Freeman, Jim, and Xin Zhao. "An SEM Approach to Modeling Housing Values." In Data Analysis and Applications 1. John Wiley & Sons, Inc., 2019. http://dx.doi.org/10.1002/9781119597568.ch9.

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Gray, David. "House Price, Consumption, and Construction Data." In Cycles in the UK Housing Economy. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63348-0_5.

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Conference papers on the topic "Housing data"

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Pirouz, Matin, and Preston McCullough. "A Data-Centric Approach to Post-Pandemic Housing Market Analysis." In 2024 IEEE 15th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON). IEEE, 2024. http://dx.doi.org/10.1109/uemcon62879.2024.10754766.

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P, Sai Pranay D., Sayed Sameer, Rayudu Charan Ranga, R. Praveen Chowdary, and K. Venkatesh. "Smart Housing Forecast: Leveraging AI Models for Predictive Pricing and Recommendations." In 2025 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI). IEEE, 2025. https://doi.org/10.1109/icdsaai65575.2025.11011695.

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Bie, Mu, Lian Tang, and Hang Lin. "Research on K-means Optimized Cluster Analysis Method for Agricultural Housing Construction Data." In 2024 IEEE 5th International Conference on Pattern Recognition and Machine Learning (PRML). IEEE, 2024. https://doi.org/10.1109/prml62565.2024.10779900.

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Bora, Shital P. "Data mining and ware housing." In 2011 3rd International Conference on Electronics Computer Technology (ICECT). IEEE, 2011. http://dx.doi.org/10.1109/icectech.2011.5941548.

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"Analysis of Housing Transaction Data." In 9th European Real Estate Society Conference: ERES Conference 2002. ERES, 2002. http://dx.doi.org/10.15396/eres2002_102.

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Li, Qingyang, Zhiwen Yu, Bin Guo, and Xinjiang Lu. "Inferring Housing Demand based on Express Delivery Data." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8621904.

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Xue, Jiguang, Chunsheng Yan, Lu Liu, Jiahan Wang, Xuliang Zhao, and Xin Wang. "ADHDP-Based Housing Energy Management For Two Housing Units With Mobile Storage." In 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2020. http://dx.doi.org/10.1109/ddcls49620.2020.9275280.

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Velthorst, Marlon, and Cicek Guven. "Predicting Housing Market Trends Using Twitter Data." In 2019 6th Swiss Conference on Data Science (SDS). IEEE, 2019. http://dx.doi.org/10.1109/sds.2019.00010.

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Xie, Zhijun, Zhenting Guo, Xiaoxia Hong, Zuoming Zhao, Juan Liu, and Xiaoling Li. "Housing Profile and Recommendation for Housing Provident Fund Using Two-Tower Neural Collaborative Filtering." In 2023 8th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA). IEEE, 2023. http://dx.doi.org/10.1109/icccbda56900.2023.10154828.

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Larrarte, Frédérique, Nicole Goutal, and Augustin Doumic. "Floods in housing estate: some experimental data." In Proceedings of the 39th IAHR World Congress From Snow to Sea. International Association for Hydro-Environment Engineering and Research (IAHR), 2022. http://dx.doi.org/10.3850/iahr-39wc252171192022577.

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Reports on the topic "Housing data"

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Davis, Lucas, Sebastian Martinez, and Bibiana Taboada. Energy-Efficient Housing in Mexico Data Set. Inter-American Development Bank, 2020. http://dx.doi.org/10.18235/0002330.

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Hobbs, Abbi, and Claire Tranter. Migrants and Housing. Parliamentary Office of Science and Technology, 2017. http://dx.doi.org/10.58248/pn560.

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Migration is often cited in public debate as a significant factor in the demand for UK housing. This POSTnote provides an overview of available research on migrants and housing. It examines definitions and data sources on migration and its implications. It also outlines the possible impact of migrants on housing, including variation by tenure type, migrant characteristics and region. Finally, it considers the impact of housing on migrants and local communities.

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Miller, JD, and RG Pratt. Estimates of Refrigerator Loads in Public Housing Based on Metered Consumption Data. Office of Scientific and Technical Information (OSTI), 1998. http://dx.doi.org/10.2172/1112.

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Bergman, Peter, Eric Chan, and Adam Kapor. Housing Search Frictions: Evidence from Detailed Search Data and a Field Experiment. National Bureau of Economic Research, 2020. http://dx.doi.org/10.3386/w27209.

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Coughlan, Ruth T. User Data Package - Energy-Efficient Windows and Window Coverings for Naval Housing. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada225885.

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Miller, J. D., and R. G. Pratt. Estimates of Refrigerator Loads in Public Housing Based on Metered Consumption Data - ERRATA. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/2888.

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Aref-Adib, Camron. The Resolution Foundation Housing Outlook Q2 2025. The Resolution Foundation, 2025. https://doi.org/10.63492/dyn226.

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Housing is fundamental to living standards. It’s no surprise that a Government committed to raising living standards has put housing at the heart of its agenda – most notably through its ambition to build 1.5 million new homes by the end of this Parliament. And it is most families’ largest expense, meaning that disposable incomes are highly sensitive to changes in rents or mortgage payments. The Resolution Foundation has today launched a new website tracking 12 indicators of how housing is affecting living standards in the UK. These include both indicators which describe the situation today –

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Medellín, Nadin, and Eduardo Rojas. Housing Policy Matters for the Poor: Housing Conditions in Latin America and the Caribbean, 1995-2006. Inter-American Development Bank, 2011. http://dx.doi.org/10.18235/0011351.

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This paper discusses the evolution of housing conditions in urban areas of Latin America and the Caribbean from 1995 to 2006 based on data from household surveys done in 18 countries that comprise 95 percent of the urban population of the region. The results indicate that, on average, the proportion of urban households facing housing shortages is declining. This decline holds for households of all income levels, particularly those in the lower quintiles of the income distribution structure. The estimates made in this study indicate that in 2006 lack of infrastructure affected almost 19 million

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Vargas, Miguel, and Fernando Lefort. Tacit Collusion in the Santiago Housing Market. Inter-American Development Bank, 2011. http://dx.doi.org/10.18235/0011304.

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Given the numerous and widely acknowledged benefits of a well-functioning housing market, it is vital to understand the degree of competition in that market, which is the starting point for undertaking any policy tool aimed at improving its efficiency. This paper tests the extent of competition in the housing market in Santiago, Chile using a two-step methodology. In the first step, using a hedonic price model, the Santiago housing market is divided into sub-markets, which are analyzed separately. The second step is the tacit collusion test itself, which compares the industry markup with the b

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Kotula, Hannah, and David C. Maré. Distinctive neighbourhood housing patterns in Aotearoa New Zealand. Motu Economic and Public Policy Research, 2024. http://dx.doi.org/10.29310/wp.2024.53.

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This paper summarises distinct housing and demographic patterns across neighbourhoods in New Zealand’s main urban areas, using data from the 2018 Census of Population and Dwellings. It uses exploratory factor analysis to classify neighbourhood types. It contributes background information for a broader research programme - WERO: Working to End Racial Oppression.

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