Section 62 of Public Act 21-2, June Special Session, as modified by Section 71 of Public Act 23-204, required the Office of Policy and Management (OPM) to conduct a “Housing and Segregation Study”. This dataset is one of the products of the Housing and Segregation Study. This dataset shows "Matrices of Segregation" calculated for various Connecticut geographies (https://www.census.gov/topics/housing/housing-patterns/guidance/appendix-b.html)

DisclaimerBefore using this layer, please review the 2018 Rochester Citywide Housing Market Study for the full background and context that is required for interpreting and portraying this data. Please click here to access the study. Please also note that the housing market typologies were based on analysis of property data from 2008 to 2018, and is a snapshot of market conditions within that time frame. For an accurate depiction of current housing market typologies, this analysis would need to be redone with the latest available data.About the DataThis is a polygon feature layer containing the boundaries of all census blockgroups in the city of Rochester. Beyond the unique identifier fields including GEOID, the only other field is the housing market typology for that blockgroup.Information from the 2018 Housing Market Study- Housing Market TypologiesThe City of Rochester commissioned a Citywide Housing Market Study in 2018 as a technical study to inform development of the City's new Comprehensive Plan, Rochester 2034, and retained czb, LLC – a firm with national expertise based in Alexandria, VA – to perform the analysis.Any understanding of Rochester’s housing market – and any attempt to develop strategies to influence the market in ways likely to achieve community goals – must begin with recognition that market conditions in the city are highly uneven. On some blocks, competition for real estate is strong and expressed by pricing and investment levels that are above city averages. On other blocks, private demand is much lower and expressed by above average levels of disinvestment and physical distress. Still other blocks are in the middle – both in terms of condition of housing and prevailing prices. These block-by-block differences are obvious to most residents and shape their options, preferences, and actions as property owners and renters. Importantly, these differences shape the opportunities and challenges that exist in each neighborhood, the types of policy and investment tools to utilize in response to specific needs, and the level and range of available resources, both public and private, to meet those needs. The City of Rochester has long recognized that a one-size-fits-all approach to housing and neighborhood strategy is inadequate in such a diverse market environment and that is no less true today. To concisely describe distinct market conditions and trends across the city in this study, a Housing Market Typology was developed using a wide range of indicators to gauge market health and investment behaviors. This section of the Citywide Housing Market Study introduces the typology and its components. In later sections, the typology is used as a tool for describing and understanding demographic and economic patterns within the city, the implications of existing market patterns on strategy development, and how existing or potential policy and investment tools relate to market conditions.Overview of Housing Market Typology PurposeThe Housing Market Typology in this study is a tool for understanding recent market conditions and variations within Rochester and informing housing and neighborhood strategy development. As with any typology, it is meant to simplify complex information into a limited number of meaningful categories to guide action. Local context and knowledge remain critical to understanding market conditions and should always be used alongside the typology to maximize its usefulness.Geographic Unit of Analysis The Block Group – a geographic unit determined by the U.S. Census Bureau – is the unit of analysis for this typology, which utilizes parcel-level data. There are over 200 Block Groups in Rochester, most of which cover a small cluster of city blocks and are home to between 600 and 3,000 residents. For this tool, the Block Group provides geographies large enough to have sufficient data to analyze and small enough to reveal market variations within small areas.Four Components for CalculationAnalysis of multiple datasets led to the identification of four typology components that were most helpful in drawing out market variations within the city:• Terms of Sale• Market Strength• Bank Foreclosures• Property DistressThose components are described one-by-one on in the full study document (LINK), with detailed methodological descriptions provided in the Appendix.A Spectrum of Demand The four components were folded together to create the Housing Market Typology. The seven categories of the typology describe a spectrum of housing demand – with lower scores indicating higher levels of demand, and higher scores indicating weaker levels of demand. Typology 1 are areas with the highest demand and strongest market, while typology 3 are the weakest markets. For more information please visit: https://www.cityofrochester.gov/HousingMarketStudy2018/Dictionary: STATEFP10: The two-digit Federal Information Processing Standards (FIPS) code assigned to each US state in the 2010 census. New York State is 36. COUNTYFP10: The three-digit Federal Information Processing Standards (FIPS) code assigned to each US county in the 2010 census. Monroe County is 055. TRACTCE10: The six-digit number assigned to each census tract in a US county in the 2010 census. BLKGRPCE10: The single-digit number assigned to each block group within a census tract. The number does not indicate ranking or quality, simply the label used to organize the data. GEOID10: A unique geographic identifier based on 2010 Census geography, typically as a concatenation of State FIPS code, County FIPS code, Census tract code, and Block group number. NAMELSAD10: Stands for Name, Legal/Statistical Area Description 2010. A human-readable field for BLKGRPCE10 (Block Groups). MTFCC10: Stands for MAF/TIGER Feature Class Code 2010. For this dataset, G5030 represents the Census Block Group. BLKGRP: The GEOID that identifies a specific block group in each census tract. TYPOLOGYFi: The point system for Block Groups. Lower scores indicate higher levels of demand – including housing values and value appreciation that are above the Rochester average and vulnerabilities to distress that are below average. Higher scores indicate lower levels of demand – including housing values and value appreciation that are below the Rochester average and above presence of distressed or vulnerable properties. Points range from 1.0 to 3.0. For more information on how the points are calculated, view page 16 on the Rochester Citywide Housing Study 2018. Shape_Leng: The built-in geometry field that holds the length of the shape. Shape_Area: The built-in geometry field that holds the area of the shape. Shape_Length: The built-in geometry field that holds the length of the shape. Source: This data comes from the City of Rochester Department of Neighborhood and Business Development.

Affordable housing plays a significant role for the wellbeing of people all over the world. However, against the background of housing commodification and market reforms since 1978 in China, housingprice in many cities especially mega cities such as Beijing, Shanghai, Shenzhen and Guanghzou in China hasundergone rapidly increasing. The fact negatively affects housing accessibility of many residents and leadsto socio-spatial polarization of many cities. Driven by this concern, this research explores the spatial distribution pattern of housing prices and the influencing factors of Shenyang, a typical old industrial city in China.Based on POI data and the Kriging method, we firstly simulated the spatial distribution pattern of housingprices in Shenyang. Then, 11 independent variables were selected (consisting of community characteristics,public facilities and public transportations) to investigate mechanisms underlying the spatial differential pattern of housing prices of Shenyang, based on the Geographically Weighted Regression model (GWR). Theresults are as following. First, the housing price of different communities in Shenyang spatially forms amulti-center structure. Changbai region has replaced Shenhe and Heping districts as the new peak price area.Second, the independent variables show significant spatial heterogeneity. Variables related to communitycharacteristics, such as ratio of green space, parking lot ratio and neighbourhoods management fees, have significant positive effects on housing price in general. Third, we found that urban housing market developmentof old industrial cities such as Shenyang has long been featured by the "strong government, weak market" development strategies.

Lebenslagen und Wohnbedingungen in ländlichen Gebieten Tansanias.

Themen: l.) Detaillierte demographische Angaben zu allen Haushaltsmitgliedern; Haupteinkommensquellen im Haushalt. 2.) Beschreibung und Ausstattung des Hauses: Materialien; Größe; Wände; Dach; Fenster, Türen; Fußboden; Toilette; Waschraum; Nutzungsart aller Gebäudeteile, Räume und umliegender Flächen; Beschreibung der Kochstelle, der Kochausstattung und des Eßplatzes; Koch- und Feuermittel; Beschreibung der eigenen Schlafstelle und der für Gäste; Aufenthaltsräume; Wasserstellen und Wasserspeicher; Möbel und Kleideraufbewahrung; Besitz und Zustand langlebiger Konsumgüter; Aufbewahrung von Werkzeugen. 3.) Tierhaltung: derzeitige Anzahl der Tiere sowie Veränderungen im vergangenen Jahr; Personal für die Tierhaltung; Einnahmen aus dem Verkauf von Tieren und Produkten. 4.) Landwirtschaft: Anbauflächen; angebaute Produkte; Erntemengen; Kosten; Lagerhaltung und Probleme; Distribution und Einnahmen; Baumbestand und Neupflanzungen; angestellte Landarbeiter. 5.) Einkommens- und Vermögenslage: Einkommensquellen; Einkommen im vergangenen Jahr; Ersparnisse und Bankkonten; Verleihungen; Schulden; Einzahlungen in die Pensionskasse und angesparter Betrag; Ausgaben der vergangenen Woche. 6.) Bau und Konstruktion des derzeitigen Wohnhauses: Baujahr und Baudauer bzw. Jahr des Erwerbs; Eigentums- und Besitzverhältnisse; weiterer Hausbesitz; Grundstückserwerb bzw. -zuteilung; Vorschriften und Vorüberlegungen beim Bau; Anlaß für den Baubeginn; Eigenleistungen beim Hausbau; Kooperation und beauftragte Handwerker; Kosten für Materialien, Transport und Handwerker; Gesamtkosten; derzeitiger Verkaufs- und Mietwert des Hauses; Finanzierung des Hausbaus; Probleme bei der Beschaffung von Materialien, Werkzeugen und Probleme mit den Handwerkern sowie Problemlösungen; voraussichtliche Haltbarkeit des Hauses und Schwachstellen; ausgeführte Reparaturen und Verbesserungen am Haus; bisherige sowie geplante Reparaturen, Verbesserungen und Neubauten.

Introduction: Despite the growing efforts to standardize coding for social determinants of health (SDOH), they are infrequently captured in electronic health records (EHRs). Most SDOH variables are still captured in the unstructured fields (i.e., free-text) of EHRs. In this study we attempt to evaluate a practical text mining approach (i.e., advanced pattern matching techniques) in identifying phrases referring to housing issues, an important SDOH domain affecting value-based healthcare providers, using EHR of a large multispecialty medical group in the New England region, United States. To present how this approach would help the health systems to address the SDOH challenges of their patients we assess the demographic and clinical characteristics of patients with and without housing issues and briefly look into the patterns of healthcare utilization among the study population and for those with and without housing challenges.Methods: We identified five categories of housing issues [i.e., homelessness current (HC), homelessness history (HH), homelessness addressed (HA), housing instability (HI), and building quality (BQ)] and developed several phrases addressing each one through collaboration with SDOH experts, consulting the literature, and reviewing existing coding standards. We developed pattern-matching algorithms (i.e., advanced regular expressions), and then applied them in the selected EHR. We assessed the text mining approach for recall (sensitivity) and precision (positive predictive value) after comparing the identified phrases with manually annotated free-text for different housing issues.Results: The study dataset included EHR structured data for a total of 20,342 patients and 2,564,344 free-text clinical notes. The mean (SD) age in the study population was 75.96 (7.51). Additionally, 58.78% of the cohort were female. BQ and HI were the most frequent housing issues documented in EHR free-text notes and HH was the least frequent one. The regular expression methodology, when compared to manual annotation, had a high level of precision (positive predictive value) at phrase, note, and patient levels (96.36, 95.00, and 94.44%, respectively) across different categories of housing issues, but the recall (sensitivity) rate was relatively low (30.11, 32.20, and 41.46%, respectively).Conclusion: Results of this study can be used to advance the research in this domain, to assess the potential value of EHR's free-text in identifying patients with a high risk of housing issues, to improve patient care and outcomes, and to eventually mitigate socioeconomic disparities across individuals and communities.

Affirmatively Furthering Fair Housing (AFFH) is a legal requirement that federal agencies and federal grantees further the purposes of the Fair Housing Act. This obligation to affirmatively further fair housing has been in the Fair Housing Act since 1968 (for further information see Title VIII of the Civil Rights Act of 1968, 42 U.S.C. 3608 and Executive Order 12892). HUD's AFFH rule provides an effective planning approach to aid program participants in taking meaningful actions to overcome historic patterns of segregation, promote fair housing choice, and foster inclusive communities that are free from discrimination. As provided in the rule, AFFH means "taking meaningful actions, in addition to combating discrimination, that overcome patterns of segregation and foster inclusive communities free from barriers that restrict access to opportunity based on protected characteristics. Specifically, affirmatively furthering fair housing means taking meaningful actions that, taken together, address significant disparities in housing needs and in access to opportunity, replacing segregated living patterns with truly integrated and balanced living patterns, transforming racially and ethnically concentrated areas of poverty into areas of opportunity, and fostering and maintaining compliance with civil rights and fair housing laws. The duty to affirmatively further fair housing extends to all of a program participant's activities and programs relating to housing and urban development."

DisclaimerBefore using this layer, please review the 2018 Rochester Citywide Housing Market Study for the full background and context that is required for interpreting and portraying this data. Please click here to access the study. Please also note that the housing market typologies were based on analysis of property data from 2008 to 2018, and is a snapshot of market conditions within that time frame. For an accurate depiction of current housing market typologies, this analysis would need to be redone with the latest available data.About the DataThis is a webmap of a polygon feature layer containing the boundaries of all census blockgroups in the city of Rochester. Beyond the unique identifier fields including GEOID, the only other field is the housing market typology for that blockgroup. The map is visualized based on market typology score with strongest market in pink, and weakest market in dark blue.Information from the 2018 Housing Market Study- Housing Market TypologiesThe City of Rochester commissioned a Citywide Housing Market Study in 2018 as a technical study to help inform development of the City's new Comprehensive Plan, Rochester 2034 , and retained czb, LLC - a firm with national expertise based in Alexandria, VA - to perform the analysis.Any understanding of Rochester’s housing market – and any attempt to develop strategies to influence the market in ways likely to achieve community goals – must begin with recognition that market conditions in the city are highly uneven. On some blocks, competition for real estate is strong and expressed by pricing and investment levels that are above city averages. On other blocks, private demand is much lower and expressed by above average levels of disinvestment and physical distress. Still other blocks are in the middle – both in terms of condition of housing and prevailing prices. These block-by-block differences are obvious to most residents and shape their options, preferences, and actions as property owners and renters. And, importantly, these differences shape the opportunities and challenges that exist in each neighborhood, the types of policy and investment tools to utilize in response to specific needs, and the level and range of available resources, both public and private, to meet those needs. The City of Rochester has long appreciated that a one-size-fits-all approach to housing and neighborhood strategy is inadequate in such a diverse market environment, and that is no less true today. To concisely describe distinct market conditions and trends across the city in this study, a Housing Market Typology was developed using a wide range of indicators to gauge market health and investment behaviors. This section of the Citywide Housing Market Study introduces the typology and its components. In later sections, the typology is used as a tool for describing and understanding demographic and economic patterns within the city, the implications of existing market patterns on strategy development, and how existing or potential policy and investment tools relate to market conditions.Overview of Housing Market Typology PurposeThe Housing Market Typology in this study is a tool for understanding recent market conditions and variations within Rochester and informing housing and neighborhood strategy development. As with any typology, it is meant to simplify complex information into a limited number of meaningful categories to guide action. Local context and knowledge remain critical to understanding market conditions and should always be used alongside the typology to maximize its usefulness.Geographic Unit of Analysis The Block Group – a geographic unit determined by the U.S. Census Bureau – is the unit of analysis for this typology, which utilizes parcel-level data. There are over 200 Block Groups in Rochester, most of which cover a small cluster of city blocks and are home to between 600 and 3,000 residents. For this tool, the Block Group provides geographies large enough to have sufficient data to analyze and small enough to reveal market variations within small areas.Four Components for CalculationAnalysis of multiple datasets led to the identification of four typology components that were most helpful in drawing out market variations within the city:• Terms of Sale• Market Strength• Bank Foreclosures• Property DistressThose components are described one-by-one on in the full study document (LINK), with detailed methodological descriptions provided in the Appendix.A Spectrum of Demand The four components were folded together to create the Housing Market Typology. The seven categories of the typology describe a spectrum of housing demand – with lower scores indicating higher levels of demand, and higher scores indicating weaker levels of demand. Typology 1 are areas with the highest demand and strongest market, while typology 3 are the weakest markets. For more information please visit: https://www.cityofrochester.gov/HousingMarketStudy2018/

This EnviroAtlas web service includes maps that illustrate the number and density of housing units. Housing density and the proximity of housing to employment can affect commuting patterns. Housing located near jobs can reduce commute time and allow for a greater variety of commute modes. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This map compares housing units by three different types: owner-occupied, renter-occupied, or vacant. Only the type with the largest count of housing units receives a color on the map.This pattern is shown by states, counties, and tracts throughout the entire US. This dataset comes from the most recent 5-year American Community Survey from the Census Bureau (ACS). The Census ACS estimates come from this current-year ACS layer from the ArcGIS Living Atlas of the World.Each year, the data values within this map is updated to reflect the newest ACS data, keeping this map up-to-date.This map helps us answer different questions such as:Are renters or home-owners more prevalent in cities? Suburbs? Rural areas?Where are vacant housing units? This question can help pinpoint blight within cities.How many housing units are within different areas?

Exploring the spatial pattern of affordable housing land and understanding the allocation logic of multi-level governments can help promote the optimal land allocation and accelerate the equalization of regional basic public services. Based on the affordable housing land in China Land Market Network, this paper uses the kernel density and geographic detector model to study the spatial pattern of affordable housing land from the perspectives of the province, city and county levels in China and analyze the influencing factors of its spatial mismatch. The results show that: 1) The allocation pattern of affordable housing land shows strong spatial differentiation at all levels, and the dense distribution and scattered distribution are obvious. 2) There are spatial mismatches in the affordable housing land among all levels. Excessive and shortage mismatches coexist, and are closely related to land factors. 3) There are differences in the influencing factors of affordable housing land spatial mismatch. Affordable housing land basically follows the allocation logic of reducing costs among provinces. However, among cities and counties, it's affected by land constraints and land cost at the same time, showing a strong logic of task completion and cost reduction. Accordingly, suggestions are given: It should reasonably evaluate the demand for affordable housing and accurately allocate land resource, the supervision and assessment system of local governments needs to be optimized to prevent the strategic behavior.

Homeowners and renters in state-subsidized apartments.

This dataset provides a comprehensive overview of new housing price indexes in Canada. The data is sourced from a reliable statistical survey, offering a detailed breakdown of housing prices across different components such as total house and land, house only, and land only. The dataset is structured to include key metrics such as geographical location, price index classification, and specific price values, providing a robust foundation for analyzing housing price dynamics within the country.

Key Features

• Price Index Metrics: The dataset includes price indexes for total house and land, house only, and land only, providing a complete picture of housing price dynamics across different components.
• Geographical Focus: Data is specific to Canada, providing insights into national housing price trends and patterns.
• Unit of Measurement: Information is presented in index units (201612=100), allowing for straightforward analysis and comparison.
• Temporal Precision: The data is time-stamped for January 1981, ensuring relevance and accuracy for temporal analysis.

Potential Uses

• Real Estate Market Analysis: Assist in understanding the housing price dynamics in Canada, which is crucial for real estate market forecasting and planning.
• Investment Decisions: Provide insights into optimal investment strategies for real estate in various regions.
• Economic Policy: Support policymakers in monitoring and ensuring compliance with housing market trends and economic standards.
• Market-Specific Insights: Evaluate the impact of housing price trends on specific regions and potential growth or decline areas.
• Strategic Planning: Inform strategic planning for real estate developers and policymakers by providing a clear snapshot of current housing price levels and trends.

Abstract

This dataset was curated for the digital humanities portion of the project "500 Years of Black History in South Florida" by Synatra Smith, Luling Huang, and Portia Hopkins.

Methodology

Data was curated at the U.S. Census Tract level for four counties in South Florida: Broward, Miami-Dade, Monroe, and Palm Beach.

There are two tables in this dataset:

• sociodem_enepov: This table contains several sociodemographic variables and energy burden level at the census tract level;
• airqua: This table contains monthly median air quality data at the census tract level for Year 2016.

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The sociodemographic data come from the American Community Survey (2020 5-year estimates). The variables include fraction of black population, median income, unemployment rate, and four education level variables for population 25 years or above: fraction of population below high school, fraction of population who had high school diploma only, fraction of population who had a college degree or equivalent only, and fraction of population who had a graduate degree. Here are the table numbers and relevant columns from the U.S. Census data portal:

• Education: S1501
• Black population: B01001B (column: B01001B_001E)
• Total population: B01001 (for calculating fraction of black population)
• Median income: S1903 (column: S1903_C03_001E)
• Employment: S2301 (column: S2301_C04_001E)

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The energy burden data come from the U.S. Department of Energy's Low-Income Energy Affordability Data (LEAD) tool. The air quality (PM2.5 concentration) data come from the U.S. Centers for Disease Control and Prevention's Daily Census Tract-Level PM2.5 Concentrations, 2016.

This project is conducted on behalf of the Association for the Study of African American Life and History and the National Park Service with additional funding from the Council on Library and Information Resources.

References

• Centers for Disease Control and Prevention. (n.d.). National Environmental Public Health Tracking Network [Data set]. www.cdc.gov/ephtracking

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• Ma, O. (2018). *Low-Income Energy Affordability Data - LEAD Tool - 2018 Update *[Data set]. U.S. Department of Energy. https://dx.doi.org/10.25984/1784729

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• U.S. Census Bureau. (2023). United States Census Bureau: Data [Data set]. U.S. Department of Commerce. https://data.census.gov/

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Usage

This dataset curates from data existing in the public domain and can be used for other purposes freely with attribution.

Waßmer, T. (2002) INFLUENCE OF HOUSING CONDITIONS ON THE HIBERNATION PATTERNS OFEUROPEAN HAMSTERS (CRICETUS CRICETUS). Submitted to and rejected by the Journal of Mammalogy. Full paper including the reviews which lead to rejection of the ms.

Urban housing location and locational amenities play an important role in median house price distribution and growth among the suburbs of many metropolitan cities in developed countries, such as Australia. In particular, distance from the central business district (CBD) and access to the transport network plays a vital role in house price distribution and growth over various suburbs in a city. However, Australian metropolitan cities have experienced increases in housing prices by up to 120% over the last 20 years, and the growth pattern was different across all suburbs in a city, such as in Melbourne. Therefore, this study examines the impacts of locational amenities on house price changes across various suburbs in Melbourne over the three census periods of 2006, 2011, and 2016, and suggests some strategic guidelines to improve the availability and accessibility of locational amenities in the suburbs with less concentrated amenities. This study chose three Local Government Areas (LGAs) of Maribyrnong, Brimbank and Wyndham in Melbourne. Each LGA has been selected as a case study because many low-income people live in these LGAs’ areas. Further, some suburbs of these LGAs have maintained similar housing prices for an extended time, while some have not.The study applied a quantitative spatial methodology to examine the housing price distribution and growth patterns by evaluating the concentration and accessibility of locational urban amenities using GIS-based techniques and a spatial data set. The spatial data analyses were performed by spatial statistics methods to measure central tendency, Local Moran’s I of LISA clustering, Kernel Density Estimation (KDE), Kernel Density Smoothing (KDS). These tests were used to find the patterns of house price distribution and growth. The study also identified the accessibility of amenities in relation to median house price distribution and growth. Spatial Autoregressive Regression (SAR), Spatial Lag, and Spatial Errors models were used to identify the spatial dependencies to test the statistical significance between the median house price and the concentration and access of local urban amenities over the three census years.This study found three median house price distribution and growth patterns among the suburbs in the three selected LGAs. There are growth differences in the median house price for different census years between 2006 and 2011, 2011 and 2016, and 2006 and 2016. The Low-High (LH) median house price distribution clusters between 2006 and 2011 became High-High (HH) clusters between the census years 2011 and 2016, and 2006 and 2016. The median house price growth rate increased significantly in the census years between 2006 and 2011. Most of the HH median house price distribution and growth clusters’ tendencies were closer to the Melbourne CBD. On the other hand, the Low-Low (LL) distribution and growth clusters were closer to Melbourne’s periphery. The suburbs located further away had low access to amenities. The HH median house price clusters are located closer to stations and educational institutes. Better access to locational amenities led to more significant HH median house price clusters, as the median house price increased at an increasing rate between 2011 and 2016. The HH median house price clusters recorded more growth between 2006 and 2016. The suburbs with train stations had better access to most other locational amenities. Almost all HH median house price clusters had train stations with higher access to amenities.There was a consistent relationship between median house price distribution, growth patterns, and locational urban amenities. The spatial lag and spatial error model tests showed that between 2006 and 2011, and 2006 and 2016, there were differences in the amenities. Still, these did not affect the outcomes in observations, and were related only to immeasurable factors for some reason. Therefore, the higher house price in the neighbouring suburb could increase the price in that suburb. The research also found from the regression analysis that highly significant amenities confirming travel time to the CBD by bus, and distance to the CBD, were negatively related in all three previous census years. This negative relationship estimates that the house price growth is lower when the distance is longer. Due to this travel to the CBD by bus is not a popular option for households. The train stations are essential for high house price growth. The house price growth is low when homes are further away from train stations and workplaces.This thesis has three contributions. Firstly, it uses the Rational Choice Theory (RCT), providing a theoretical basis for analysing households’ mutually interdependent preferences of urban amenities that are found to regulate house price growth clusters. Secondly, the methodological contribution uses the GIS-defined cluster mapping and spatial statistics in queries and reasoning, measurements, transformations, descriptive summaries, optimisation, and hypothesis testing models between house price distribution and growth, and access to urban locational amenities. Thirdly, this research contributes to designing practical guidelines to identify local urban amenities for planning local area development.Overall, this thesis demonstrates that the median house price distribution and growth patterns are highly correlated with the concentration and accessibility of locational urban amenities among the suburbs in three selected LGAs in Melbourne over the three census years (i.e., 2006, 2011, and 2016). The findings bring to the fore the need for research at the local and state levels to identify specific amenities relevant to the middle-class house distribution strategy, which can be helpful for investors, estate agents, town planners, and builders as partners for effective local development. The future study might use social, psychological, and macroeconomic variables not considered or used in this research.

UNIDO pub on use of timber for housing construction in developing countries, with special reference to traditional building design - covers (1) the critical shortage of adequate dwellings; problems of providing appropriate building materials; need for use of available timber resources; the need for information on wood technology; house planning principles and design procedures (2) factors affecting structural efficiency; non-structural components and parts; production and erection techniques; comparative costs of timber-frame and other houses.

The Rochester 2018 Housing Market Study was commissioned to aid the City in understanding the patterns of housing investment and disinvestment since 2007 and formulate actionable strategies to include in the comprehensive plan, Rochester 2034. The study drew upon a wide range of quantitative sources, including U.S. Census Bureau and local data and qualitative sources including stakeholder focus groups and expert interviews. The study contains four parts:Part 1: Rochester's Housing MarketPart 2: Stretegic Direction for Rochester's Housing MarketPart 3: Housing Market InterventionsPart 4: Recommendations

Housing property is the most important position in a household’s wealth portfolio. Even though there is strong evidence that house price cycles and saving patterns behave synchronously, the underlying causes remain controversial. The present paper examines if there is a wealth effect of house prices on savings using household-level panel data from the German Socio-Economic Panel for the period 1996-2012. We find that young homeowners decrease their savings in response to unanticipated house price shocks, whereas old households hardly respond to house price changes. Although effects are relatively low at magnitude, we interpret this as evidence of a housing wealth effect.

This dataset's primary purpose is to classify house and commercial space advertisements based on the clarity of their descriptions. It provides a valuable resource for understanding and identifying vague or unclear property listings, which is crucial for improving communication in online real estate platforms. The data facilitates the development of automated systems that can assess the quality of ad content.

Columns

• description: This column contains the textual content of the property advertisements. These descriptions originate from various sources and are the subject of classification.
• Vague/Not: This column indicates the clarity status of the corresponding description. A value of 0 signifies that the advertisement description is considered vague, while a value of 1 denotes that it is not vague, meaning it is clear.

Distribution

The dataset is typically structured in a tabular format, expected to be presented as a CSV file. While the exact total number of rows or records across the entire dataset is not specified in the provided information, the data includes counts for different label distributions, such as 440 instances for lower vagueness scores and 385 for higher clarity scores, alongside 827 other categorised items. Specific numbers for the overall dataset size are not explicitly available.

Usage

This dataset is ideally suited for several applications, including: * Developing and refining Natural Language Processing (NLP) models for text classification and clarity assessment. * Creating tools for automatic detection of ambiguous or vague language in property advertisements. * Improving the user experience on online rental and real estate marketplaces by ensuring clearer listings. * Researching patterns of linguistic clarity and ambiguity within real estate advertising content. * Training machine learning algorithms for content moderation or quality control in e-commerce and online transaction platforms.

Coverage

The dataset specifically pertains to New York room rental advertisements, indicating a geographic focus on this city. The sources do not provide details regarding the exact time range when the data was collected or any specific demographic scope.

License

CCO

Who Can Use It

• Data scientists and machine learning engineers focused on building and deploying text classification and NLP models.
• Researchers in the fields of natural language processing, linguistics, and real estate market analysis.
• Developers and product managers for online rental platforms and real estate agencies seeking to enhance their listing quality and user satisfaction.
• Businesses operating in the e-commerce, property management, or accommodation sectors that rely on clear communication in their advertising.

Dataset Name Suggestions

• New York Property Ad Clarity Dataset
• NYC Rental Ad Ambiguity Classifier
• Property Advertisement Vague/Not Classification
• New York Housing Description Clarity Dataset

Attributes

Original Data Source: Newyork Room Rental Ads

Serious searchers (potential house buyers).