This dataset includes the listing prices for the sale of properties (mostly houses) in Ontario. They are obtained for a short period of time in July 2016 and include the following fields: - Price in dollars - Address of the property - Latitude and Longitude of the address obtained by using Google Geocoding service - Area Name of the property obtained by using Google Geocoding service

This dataset will provide a good starting point for analyzing the inflated housing market in Canada although it does not include time related information. Initially, it is intended to draw an enhanced interactive heatmap of the house prices for different neighborhoods (areas)

However, if there is enough interest, there will be more information added as newer versions to this dataset. Some of those information will include more details on the property as well as time related information on the price (changes).

This is a somehow related articles about the real estate prices in Ontario: http://www.canadianbusiness.com/blogs-and-comment/check-out-this-heat-map-of-toronto-real-estate-prices/

I am also inspired by this dataset which was provided for King County https://www.kaggle.com/harlfoxem/housesalesprediction

The Scotland Heat Map provides estimates of annual heat demand for almost 3 million properties in Scotland. Demand is given in kilowatt-hours per year (kWh/yr). Property level estimates can be combined to give values for various geographies. Both domestic and non-domestic properties are included. This raster dataset gives the total estimated heat demand of properties within 50m x 50m grid squares covering all of Scotland. Heat demand is calculated by combining data from a number of sources, ensuring that the most appropriate data available is used for each property. The data can be used by local authorities and others to identify or inform opportunities for low carbon heat projects such as district heat networks. The Scotland Heat Map is produced by the Scottish Government. The most recent version is the Scotland Heat Map 2022, which was released to local authorities in November 2023. More information can be found in the documentation available on the Scottish Government website: https://www.gov.scot/publications/scotland-heat-map-documents/

In 2006, the average monthly shelter cost for renter households was $728 and for owner households it was $998. For owners without a mortgage it was lower at $455 and for owners with a mortgage it was higher at $1393. Shelter costs are the average monthly total of all shelter expenses paid by households to secure shelter. Shelter costs for owners may include the mortgage payment, property taxes, condominium fees and the costs of electricity, heat and municipal services. Shelter costs for renters may include rent and the costs of electricity, heat and municipal services. The map shows by census subdivision the average owner’s major (monthly) payments on shelter costs for households without a mortgage.

Georeferenced Amsterdam Rental Values

Exploring Urban Housing Markets Through Historical Patterns

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About this dataset

This dataset provides insightful and comprehensive information on the spatial distribution of rental values in Amsterdam throughout a period of time. In order to generate this data, the Verponding registration from Amsterdam City Archives was consulted, which collected a tax known as the Verpondings-quohieren van den 8sten penning on the rental value of immovable property. This data was attained through transcribing and geo-referencing registration books from the archives; thereby incorporating both transcribed rental values of all real estate properties listed therein as well as geo-referenced tax records plotted onto an historical map of Amsterdam.

The compilation and analysis of historic rental values may offer further insights into underlying social, economic, and cultural developments within Amsterdam over time. Therefore, the potential applications for this dataset are enormous; offering investigators an opportunity to gather useful information with relation to urban renewal efforts or even supporting archaeological research studies. Moreover, with various columns such as order number, wijk district where applicable property is located within respective street name as well as details on whether said property is available for rent/own disposition - researchers may also utilize these collected metrics for meaningful planning/management decisions simultaneously unfolding hidden patterns concerning disparities or trends that might be discerned when compared to current trends employed by residents today

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How to use the dataset

This dataset provides insight into the spatial distribution of rental values in Amsterdam between 1647 and 1652. The data provided is a valuable resource for researchers looking to study the economic, social, and cultural history of Amsterdam over this period in time. With this data set, users can explore hidden patterns, disparities, and trends that may inform decision-making or help with urban renewal projects. Moreover, this dataset can also be used to assess archaeological and cultural heritage research.

In order to understand the georeferenced rental values better and draw meaningful conclusions from the data set it is important to keep few things in mind: - Check out handy columns such as ‘wijk’ (district) which offers information about where each property is located;
- The ‘rent/own’ indicates whether a property was rented (huur) or owned (koop);
- The ‘value’ column contains information regarding the rental value of each property; - The ‘tax’ column shows how much tax was paid on each listed property;
- In addition to this additional notes have been provided in some cases offering more insights into particular properties;

By seeing all these details together one will get an excellent overview of individual households renting or owning their real estate properties along with their tax payment throughout Amsterdam during this period 1647-1652. Additionally by graphing this data one could compare rental value against geographic location or even track consecutive years on how they vary year after year! This can help trace any historical changes taking place how they affect individual households within Amsterdam as well as socio-economic changes occurring throughout the city over the years!

Research Ideas

• Creating a predictive heat map by analyzing correlation between rental values and various other factors such as geographic location, proximity to public transportation, availability of amenities/services etc.
• Comparing and contrasting current maps of real estate prices in Amsterdam with the maps from this dataset to analyze shifts in property prices over time and understand the evolution of urban housing markets in the city.
• Studying socio-economic differences between different geographical areas based on rental values from this dataset, which could help provide a better understanding of the social, economic, and cultural history of the city

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permi...

Explore active listings and real-time home values for houses, condominiums, and townhomes in Hot Springs Village AR See prices, sizes, and property types on an interactive map.

Real Estate Rentals in Ecuador

Analyzing Location, Price, and Amenities

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About this dataset

This dataset provides a comprehensive overview of rental properties in Ecuador. It contains a wealth of information about the properties, such as their titles and locations, as well as the number of bedrooms, bathrooms and garages within them. Furthermore, it also includes valuable data points like area size to aid informed decisions for those looking to rent or lease property within the country. The data can be used for various reasons such as analyzing trends in properties offered for rent and looking into pricing differences between regions or localities. It is an invaluable resource for anyone interested in real estate within Ecuador and beyond!

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How to use the dataset

This dataset is an ideal starting point for anyone looking to dive into the rental market in Ecuador. With this data, you can explore the different rental properties, look at their prices and features and compare them with other properties in the same area. Additionally, it gives you insight into what type of property would best suit your needs and budget, as well as how many bedrooms and bathrooms are necessary to get your desired living space.

To use this dataset effectively, start by selecting specific columns that correspond to important information such as location (Provincia), price (Precio) or number of bedrooms & bathrooms (Num. dormitorios & Num. banos). With these columns selected, run some analysis on the data such as averages or mode/median values for each selection of parameters; this will give you a general idea on pricing within certain areas or specific types of houses/apartments available for rent in Ecuador. You may also wish to include all variables within your analysis; this will give more comprehensive insights about which variables are impacting price the most in a given area, allowing for further comparisons between different regions throughout Ecuador . With these tools at your disposal you'll have all the info needed to decipher which properties will fit your needs without sacrificing quality!

Research Ideas

• Use this dataset to determine the average rental costs in different provinces of Ecuador, which can be used to inform the user on how much they should expect to pay for rent when visiting or relocating.
• Analyze and compare rental prices within a certain city or neighborhood by using the data provided on rental properties in that area.
• Generate heat maps that show the variation in prices across different areas based on specific criteria such as size, number of bedrooms, etc., which could give users a better understanding of where it is most affordable and valuable to buy or rent property in Ecuador

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: real_state_ecuador_dataset.csv | Column name | Description | |:---------------------|:--------------------------------------------------------| | Titulo | Title of the rental property. (String) | | Precio | Price of the rental property. (Numeric) | | Provincia | Province where the rental property is located. (String) | | Lugar | Location of the rental property. (String) | | Num. dormitorios | Number of bedrooms in the rental property. (Numeric) | | Num. banos | Number of bathrooms in the rental property. (Numeric) | | Area | Area of the rental property. (Numeric) | | Num. garages | Number of garages in the rental property. (Numeric) |

Acknowledgements

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

The 'Climate Just' Map Tool shows the geography of England’s vulnerability to climate change at a neighbourhood scale. The Climate Just Map Tool shows which places may be most disadvantaged through climate impacts. It aims to raise awareness about how social vulnerability combined with exposure to hazards, like flooding and heat, may lead to uneven impacts in different neighbourhoods, causing climate disadvantage. Climate Just Map Tool includes maps on: Flooding (river/coastal and surface water) Heat Fuel poverty. The flood and heat analysis for England is based on an assessment of social vulnerability in 2011 carried out by the University of Manchester. This has been combined with national datasets on exposure to flooding, using Environment Agency data, and exposure to heat, using UKCP09 data. Data is available at Middle Super Output Area (MSOA) level across England. Summaries of numbers of MSOAs are shown in the file named Climate Just-LA_summaries_vulnerability_disadvantage_Dec2014.xls Indicators include: Climate Just-Flood disadvantage_2011_Dec2014.xlsx Fluvial flood disadvantage indexPluvial flood disadvantage index (1 in 30 years)Pluvial flood disadvantage index (1 in 100 years)Pluvial flood disadvantage index (1 in 1000 years) Climate Just-Flood_hazard_exposure_2011_Dec2014.xlsx Percentage of area at moderate and significant risk of fluvial floodingPercentage of area at risk of surface water flooding (1 in 30 years)Percentage of area at risk of surface water flooding (1 in 100 years)Percentage of area at risk of surface water flooding (1 in 1000 years) Climate Just-SSVI_indices_2011_Dec2014.xlsx Sensitivity - flood and heatAbility to prepare - floodAbility to respond - floodAbility to recover - floodEnhanced exposure - floodAbility to prepare - heatAbility to respond - heatAbility to recover - heatEnhanced exposure - heatSocio-spatial vulnerability index - floodSocio-spatial vulnerability index - heat Climate Just-SSVI_indicators_2011_Dec2014.xlsx % children < 5 years old% people > 75 years old% people with long term ill-health/disability (activities limited a little or a lot)% households with at least one person with long term ill-health/disability (activities limited a little or a lot)% unemployed% in low income occupations (routine & semi-routine)% long term unemployed / never worked% households with no adults in employment and dependent childrenAverage weekly household net income estimate (equivalised after housing costs) (Pounds)% all pensioner households% households rented from social landlords% households rented from private landlords% born outside UK and IrelandFlood experience (% area associated with past events)Insurance availability (% area with 1 in 75 chance of flooding)% people with % unemployed% in low income occupations (routine & semi-routine)% long term unemployed / never worked% households with no adults in employment and dependent childrenAverage weekly household net income estimate (equivalised after housing costs) (Pounds)% all pensioner households% born outside UK and IrelandFlood experience (% area associated with past events)Insurance availability (% area with 1 in 75 chance of flooding)% single pensioner households% lone parent household with dependent children% people who do not provide unpaid care% disabled (activities limited a lot)% households with no carCrime score (IMD)% area not roadDensity of retail units (count /km2)% change in number of local VAT-based units% people with % not home workers% unemployed% in low income occupations (routine & semi-routine)% long term unemployed / never worked% households with no adults in employment and dependent childrenAverage weekly household net income estimate (Pounds)% all pensioner households% born outside UK and IrelandInsurance availability (% area with 1 in 75 chance of flooding)% single pensioner households% lone parent household with dependent children% people who do not provide unpaid care% disabled (activities limited a lot)% households with no carTravel time to nearest GP by walk/public transport (mins - representative time)% of at risk population (no car) outside of 15 minutes by walk/public transport to nearest GP Number of GPs within 15 minutes by walk/public transport Number of GPs within 15 minutes by car Travel time to nearest hospital by walk/public transport (mins - representative time)Travel time to nearest hospital by car (mins - representative time)% of at risk population outside of 30 minutes by walk/PT to nearest hospitalNumber of hospitals within 30 minutes by walk/public transport Number of hospitals within 30 minutes by car % people with % not home workersChange in median house price 2004-09 (Pounds)% area not green space Area of domestic buildings per area of domestic gardens (m2 per m2)% area not blue spaceDistance to coast (m)Elevation (m)% households with the lowest floor level: Basement or semi-basement% households with the lowest floor level: ground floor% households with the lowest floor level: fifth floor or higher