Context

The datasets contain real estate listings in Argentina, Colombia, Ecuador, Perú, and Uruguay. With information on number of rooms, districts, prices, etc. They include houses, apartments, commercial lots, and more.

The datasets origin from Properati Data which is a data division of Properati, the Latin American property search site. On their website you can find links to different tools and datasets to use freely for your projects. All you have to do is make sure you credit them for the data.

Content

What a minute the dataset is in Spanish?! Yes, so for that reason I have provided a translated overview below. Keep in mind that although Spanish is a single language, certain words and expressions may vary depending on the country and region, e.g. the word for apartment in Colombia "apartamento" is "departamento" in Argentina. But all of these are easy to translate with Google Translator.

Overview of Data

• type - Type of listing:
  • Propiedad (Property).
  • Desarrollo/Proyecto (Development/Project).
• country - Country in which the listing is published:
  • Argentina
  • Colombia
  • Ecuador
  • Perú
  • Uruguay
• id - id of the listing. It is not unique: if the listing is updated by the real estate agency (new version of the listing) a new record is created with the same id but different dates: registration and cancellation.
• start_date - Date of registration of the listing.
• end_date - Cancellation date of the listing.
• created_on - Date of registration of the first version of the listing.
• lat - Latitude of the property.
• lon - Longitud of the property.
• l1 - Administrative Level 1: Country of the property.
• l2 - Administrative Level 2: Usually the province of the property.
• l3 - Administrative Level 3: Usually the city of the property.
• l4 - Administrative Level 4: Usually the neighbourhood of the property.
• operation - Type of listing:
  • Venta (Sale).
  • Alquiler (Rent).
• type - Type of property:
  • Casa (House).
  • Departamento (Apartment).
  • PH (Horizontal Property).
• rooms - Number of rooms (useful for Argentina).
• bedrooms - Number of bedrooms (useful for the rest of the countries).
• bathrooms - Number of bathrooms.
• surface_total - Total area in m².
• surface_covered - Area covered in m².
• price - Price published in the listing.
• currency - Currency of published price.
• price_period - Payment periods:
  • Diario (Daily).
  • Semanal (Weekly).
  • Mensual (Monthly).
• title - Title of the listing (These are in Spanish).
• description - Description of the listing (In Spanish).
• status - Development status (Completed, Under construction, ...).
• name - Development name.
• short_description - Short listing description.

Acknowledgements & Inspiration

I want to thank Properati Data for providing the datasets free of charge. Especially, datasets on real estate listings that can be difficult to come by without spending time on creating crawlers and finding websites that will allow for crawling.

The inspiration and reason I came by the datasets in the first place was through my personal project on predicting apartment prices in Buenos Aires.

Additional Information

Data was downloaded the May 24 2020.

This dataset contains the final output from the Step 2 analysis.DataStep One of this analysis primarily relies on travel data acquired through Replica.10 This dataset is produced by an activity-based model, calibrated locally with ground truth data from a diverse set of third-party source data such as mobile location data, consumer marketing data, geographic and land use data, credit card transaction data, built environment, and economic activity. Trip data provided by Replica platform includes information such as origin, destination, land use, trip purpose, and socio-demographic of the trip taker. Step Two of this analysis relies on a variety of parcel-level data from member jurisdictions, MWCOG, and DOE charging station data. This data includes existing charging stations11, Equity Emphasis Areas (EEA), Alternative Fuel Corridors (AFCs), transit stations, multifamily housing, EV Charging Justice40 Map, and MWCOG Regional Activity Centers (RACs).Step OneStep One relies primarily on travel data at the census block group level. Census block groups are scored based on trip characteristics that end within that group. Trip characteristics considered in Step One include:Trip purposeTrip lengthDwelling time (30 to 60 minutes, 60 to 120 minutes, and greater than 120 minutes)Income of tripTrips originating from multifamily housingTrips originating from equity emphasis areasThree different trip characteristic scenarios were completed in Step One, outlined in below.Prioritizing DCFCs with High Utilization: This scenario weights trips taken by people with higher incomes more heavily. Because people with higher incomes are also more likely to be homeowners with access to home charging, this scenario would focus on building out DCFCs to provide opportunities for public charging that would help serve a larger number of vehicles more quickly. Scoring adjustments in Step two provide a check on recommending an overbuilding of DCFCs in wealthier areas that already have ample access to public charging.Prioritizing Level 2 Chargers with Equity Focus: DCFCs require higher upfront costs for equipment, installation, and potential utility upgrades that may be needed to accommodate higher powered charging infrastructure. The cost of the electricity at the point of purchase is also higher, which can cause some service providers to cite economic infeasibility when deciding whether to cite DCFCs in communities with less EVs and lower utilization. Most Level 2 charging infrastructure will not require grid or electrical service upgrades, and the projects will have lower costs across other factors (e.g., equipment costs, electricity pricing for customers). Prioritizing Level 2 charging will mean there are fewer barriers to entry for a jurisdiction or project team looking to build out their charging network in EEAs.Prioritizing DCFCs for Multi-Family Housing: Individuals living in multi-family housing that don’t have a dedicated parking spot or reliable access to at-home charging, opportunity charging with DCFCs and workplace charging are two available options. Multi-family residents are more likely to use DCFC stations. Establishing DCFC charging hubs near higher concentrations of multi-family housing developments could provide an attractive and highly utilized alternative to on-site charging for buildings where it is challenging to install and maintain charging infrastructure.Each CBG is scored based on the percentage of regional trips it receives meeting the criteria. The final Step 1 analysis assigns each CBG in the study area a score of 1 to 6. The higher the CBG score, the more traffic a CBG experiences. For example, if a CBG with a score of 1 has a low number of trips starting or ending there, whereas a CBG with a score of 6 has a very high number of trips starting or ending there.Step TwoOnce the census block groups have been scored, individual parcels within high-scoring census block groups are evaluated based on characteristics that make that parcel more or less desirable for charging infrastructure. Those characteristics, called proximity score modifiers, include a parcel’s distance to existing charging stations, distance to multi-family housing, distance to highway on- and off-ramps, proximity to environmental justice communities, and distance to park-and-ride locations. These proximity score modifiers have been selected for the following reasons:Distance to existing charging stations. Locations that are close to existing public chargers have already begun to be built out and may have less demand.Distance to MFH. Residents of MFH typically lack access to home charging and will rely on public infrastructure to meet charging needs.Distance to highway on-ramp or off-ramp. Sites located near highway ramps are likely to attract EV drivers who are making longer trips, typically needing DCFC.Location in or near an EEA. Ensuring the benefits of EVs are spread equitably in the region is a priority. Providing access to charging infrastructure in or near EEAs can help remove barriers to EV adoption.Distance to park-and-ride locations. The distance from potential sites to the nearest public transportation stop with park-and-ride lots is calculated to determine which sites will be most useful in enabling more sustainable first and last miles of multimodal trips. Charging locations near transit stops could benefit EV ride-sharing companies or commuters that use a combination of personal vehicles and mass transit.Each proximity score modifier can increase or decrease a parcel’s overall score. If a parcel is not located near any proximity score modifier, their final score will not be influenced by these characteristics. Proximity score modifiers and their associated point values are as follows:Within ¼ mile of a Park-and-Ride Location - Parcel score increases by 1Within ¼ mile of MFH - Parcel score increases by 1Within ¼ mile of an EEA - Parcel score increases by 1Within ½ mile of Existing Level 2 Charging Stations - Parcel score decreases by up to 2 pointsWithin ½ mile of Existing DCFC Stations - Parcel score decreases by up to 4 pointsThese factors are assessed with GIS software and compiled to modify the parcel’s charging demand score. A parcel’s final score in Step 2 is determined by the following formula:Parcel Score = [Step 1 Census Block Group Score] + [Proximity Score Modifier Total]Parcels that are better suited for charging will score higher than parcels less suitable for charging. Each high scoring parcel should be further reviewed to determine suitability for public EV charging stations such as parcel size, parking availability, facility access, potential site host partnerships, and electric utility service capacity. Local knowledge is key to understanding results.

Housing under construction in Canada 2006-2023 Published by Fernando de Querol Cumbrera, Jun 7, 2024 In 2023, there were approximately 354,730 housing units under construction in population centers of over 10,000 people in Canada. Those numbers for 2023 were lower than the figures a year earlier. However, during most of the period considered the number of homes under construction generally increased, as there were less than 145,000 homes under construction in 2009. Development of residential construction in Canada The number of housing starts overall has developed similarly, but it was somewhat more volatile. Generally, the cities with the largest populations, like Toronto and Montreal experience the highest number of construction starts. The construction industry remains vital to Canada’s economy, providing employment to people across the country and billions in economic output. Types of housing Although many Canadians were living in single-detached houses, their cities have started to embrace multifamily starts quite early and are moving away from single family residential construction. The younger demographics in Canada, including new Canadians and young families are often striving towards homeownership but are also aware of climate change. High energy performance housing often comes with a higher price tag, but efforts are being made nationally in order to enshrine affordability as a core objective within national building codes.

This table contains data described by the following dimensions (Not all combinations are available): Geography (11 items: Canada; Prince Edward Island; Nova Scotia; Newfoundland and Labrador ...), Housing estimates (3 items: Housing starts; Housing under construction; Housing completions ...), Type of unit (6 items: Total units; Semi-detached; Single-detached; Multiples ...).

This dataset contains property sales data, including information such as PropertyID, property type (e.g., Commercial or Residential), tax keys, property addresses, architectural styles, exterior wall materials, number of stories, year built, room counts, finished square footage, units (e.g., apartments), bedroom and bathroom counts, lot sizes, sale dates, and sale prices. Explore this dataset to gain insights into real estate trends and property characteristics.

Data.milwaukee.gov, (2023). Property Sales Data. [online] Available at: https://data.milwaukee.gov [Accessed 9th October 2023].

Open Definition. (n.d.). Creative Commons Attribution 4.0 International Public License (CC BY 4.0). [online] Available at: http://www.opendefinition.org/licenses/cc-by [Accessed 9th October 2023].

Explore the housing and building statistics of the Emirate of Dubai with this comprehensive dataset, including information on investment villas, industrial buildings, multi-story buildings, and more.

Investment Villa, Industrial Building, Multi-Story Buildings, Industrial Buildings, Urban, Value Added, Total Buildings, Other (Shed - Sandaka - Caravan), Intermediate Consumption, By Area, Number of Stores, Floor Area Ratio Building, All Area, Annual, Part of Arabic House, Floor Area Ratio Buildings, Collective Household, Investment Villas, Output, Public Commercial Buildings, Capital Formation, Building Permits Issued, New Building, Room, Total Housing Units, Private Villa, Other, Compensation of Workers, Total Completed Buildings, Number of Residential Apartments, Commercial Building, Private Villas, Multi-Story Building, Attached to Villa, Apartment, Villa, Number, Arabic House, Million AED, One-Story Building, Number of Workers, Building Permits Issued, Additions and Amendments, Rural, Suqure Feet

United Arab EmiratesFollow data.kapsarc.org for timely data to advance energy economics research..

This dataset includes three tables which were custom ordered from Statistics Canada. There is a table each for Vancouver CMA, Montreal CMA, and Toronto CMA, and the tables contain variables regarding dwelling characteristics, tenure, and shelter cost. The dataset is in Beyond 20/20 (.ivt) format. The Beyond 20/20 browser is required in order to open it. This software can be freely downloaded from the Statistics Canada website: https://www.statcan.gc.ca/eng/public/beyond20-20 (Windows only). For information on how to use Beyond 20/20, please see: http://odesi2.scholarsportal.info/documentation/Beyond2020/beyond20-quickstart.pdf https://wiki.ubc.ca/Library:Beyond_20/20_Guide Custom order from Statistics Canada includes the following dimensions and variables: Geography: Montreal CMA, Vancouver CMA, Toronto CMA to the census tract level Total Shelter Cost: Under $500 to over $3000 in $500 intervals Shelter Cost to-Income Ratio: Spending less than 15%, 15-30%, 30-50%, 50% or more Tenure: Owner (including presence of mortgage), renter, subsidized housing, not subsidized housing Condominium Status: Condominium, not a condominium Household Size: 1 person, 2 persons, 3 or more people Number of Bedrooms: No bedroom or 1 bedroom, 2 or more bedrooms Structural Type: -Single detached house -Apartment with 5 or more stories -Semi-detached house, row house or other single detached house -Apartment or flat in a duplex -Apartment, building with fewer than 5 stories Household Income: Median income and average income only Original file names: EO3091_Table1_Montreal.ivt EO3091_Table1_Toronto.ivt EO3091_Table1_Vancouver.ivt

This table contains data described by the following dimensions (Not all combinations are available): Geography (37 items: Census metropolitan areas; Saguenay; Quebec; Calgary; Alberta; Edmonton; Alberta ...).

Negative Equity in U.S. Housing Market

Measuring Home Values, Debt, and Credit Risk

By Zillow Data [source]

About this dataset

This dataset, Negative Equity in the US Housing Market, provides an in-depth look into the negative equity occurring across the United States during this single quarter. Included are metrics such as total amount of negative equity in millions of dollars, total number of homes in negative equity, percentage of homes with mortgages that are in negative equity and more. These data points provide helpful insights into both regional and national trends regarding the prevalence and rate of home mortgage delinquency stemming from a diminishment of value from peak levels.

Home types available for analysis include 'all homes', condos/co-ops, multifamily units containing five or more housing units as well as duplexes/triplexes. Additionally, Cash buyers rates for particular areas can also be determined by referencing this collection. Further metrics such as mortgage affordability rates and impacts on overall indebtedness are readily calculated using information related to Zillow's Home Value Index (ZHVI) forecast methodology and TransUnion data respectively.

Other variables featured within this dataset include characteristics like region type (i.e city, county ..etc), size rank based on population values , percentage change in ZHVI since peak levels as well as loan-to-value ratio greater than 200 across all regions constituted herein (NE). Moreover Zillow's own Secondary Mortgage Market Survey data is utilized to acquire average mortgage quote rates while correlative Census Bureau NCHS median household income figures represent typical assessable proportions between wages and debt obligations . So whether you're looking to assess effects along metro lines or detailed buffering through zip codes , this database should prove sufficient for insightful explorations! Nonetheless users must strictly adhere to all conditions encompassed within Terms Of Use commitments put forth by our lead provider before accessing any resources included herewith

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Research Ideas

• Analyzing regional and state trends in negative equity: Analyze geographic differences in the percentage of mortgages “underwater”, total amount of negative equity, number of homes at least 90 days late, and other key indicators to provide insight into the factors influencing negative equity across regions, states and cities.
• Tracking the recovery rate over time: Track short-term changes in numbers related to negative equity (e.g., region or area ZHVI Change from Peak) to monitor recovery rates over time as well as how different policy interventions are affecting homeownership levels in affected areas.
• Exploring best practices for promoting housing affordability: Compare affordability metrics (e.g., mortgage payments, price-to-income ratios) across different geographic locations over time to identify best practices for empowering homeowners and promoting stability within the housing market while reducing local inequality impacts related to availability of affordable housing options and access to credit markets like mortgages/loans etc

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: NESummary_2017Q1_Public.csv | Column name | Description | |:------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------| | RegionType | The type of region (e.g., city, county, metro etc.) (String) | | City | Name of the city (String) | | County | Name of the county (String) | | State | Name of the state (String) | | Metro ...