House prices grew year-on-year in most states in the U.S. in the third quarter of 2024. The District of Columbia was the only exception, with a decline of ***** percent. The annual appreciation for single-family housing in the U.S. was **** percent, while in Hawaii—the state where homes appreciated the most—the increase exceeded ** percent. How have home prices developed in recent years? House price growth in the U.S. has been going strong for years. In 2024, the median sales price of a single-family home exceeded ******* U.S. dollars, up from ******* U.S. dollars five years ago. One of the factors driving house prices was the cost of credit. The record-low federal funds effective rate allowed mortgage lenders to set mortgage interest rates as low as *** percent. With interest rates on the rise, home buying has also slowed, causing fluctuations in house prices. Why are house prices growing? Many markets in the U.S. are overheated because supply has not been able to keep up with demand. How many homes enter the housing market depends on the construction output, whereas the availability of existing homes for purchase depends on many other factors, such as the willingness of owners to sell. Furthermore, growing investor appetite in the housing sector means that prospective homebuyers have some extra competition to worry about. In certain metros, for example, the share of homes bought by investors exceeded ** percent in 2024.

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

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

Extract detailed property data points — address, URL, prices, floor space, overview, parking, agents, and more — from any real estate listings. The Rankings data contains the ranking of properties as they come in the SERPs of different property listing sites. Furthermore, with our real estate agents' data, you can directly get in touch with the real estate agents/brokers via email or phone numbers.

A. Usecase/Applications possible with the data:

1. Property pricing - accurate property data for real estate valuation. Gather information about properties and their valuations from Federal, State, or County level websites. Monitor the real estate market across the country and decide the best time to buy or sell based on data

2. Secure your real estate investment - Monitor foreclosures and auctions to identify investment opportunities. Identify areas within special economic and opportunity zones such as QOZs - cross-map that with commercial or residential listings to identify leads. Ensure the safety of your investments, property, and personnel by analyzing crime data prior to investing.

3. Identify hot, emerging markets - Gather data about rent, demographic, and population data to expand retail and e-commerce businesses. Helps you drive better investment decisions.

4. Profile a building’s retrofit history - a building permit is required before the start of any construction activity of a building, such as changing the building structure, remodeling, or installing new equipment. Moreover, many large cities provide public datasets of building permits in history. Use building permits to profile a city’s building retrofit history.

5. Study market changes - New construction data helps measure and evaluate the size, composition, and changes occurring within the housing and construction sectors.

6. Finding leads - Property records can reveal a wealth of information, such as how long an owner has currently lived in a home. US Census Bureau data and City-Data.com provide profiles of towns and city neighborhoods as well as demographic statistics. This data is available for free and can help agents increase their expertise in their communities and get a feel for the local market.

7. Searching for Targeted Leads - Focusing on small, niche areas of the real estate market can sometimes be the most efficient method of finding leads. For example, targeting high-end home sellers may take longer to develop a lead, but the payoff could be greater. Or, you may have a special interest or background in a certain type of home that would improve your chances of connecting with potential sellers. In these cases, focused data searches may help you find the best leads and develop relationships with future sellers.

How does it work?

• Analyze sample data
• Customize parameters to suit your needs
• Add to your projects
• Contact support for further customization

Overview: This dataset was collected and curated to support research on predicting real estate prices using machine learning algorithms, specifically Support Vector Regression (SVR) and Gradient Boosting Machine (GBM). The dataset includes comprehensive information on residential properties, enabling the development and evaluation of predictive models for accurate and transparent real estate appraisals.Data Source: The data was sourced from Department of Lands and Survey real estate listings.Features: The dataset contains the following key attributes for each property:Area (in square meters): The total living area of the property.Floor Number: The floor on which the property is located.Location: Geographic coordinates or city/region where the property is situated.Type of Apartment: The classification of the property, such as studio, one-bedroom, two-bedroom, etc.Number of Bathrooms: The total number of bathrooms in the property.Number of Bedrooms: The total number of bedrooms in the property.Property Age (in years): The number of years since the property was constructed.Property Condition: A categorical variable indicating the condition of the property (e.g., new, good, fair, needs renovation).Proximity to Amenities: The distance to nearby amenities such as schools, hospitals, shopping centers, and public transportation.Market Price (target variable): The actual sale price or listed price of the property.Data Preprocessing:Normalization: Numeric features such as area and proximity to amenities were normalized to ensure consistency and improve model performance.Categorical Encoding: Categorical features like property condition and type of apartment were encoded using one-hot encoding or label encoding, depending on the specific model requirements.Missing Values: Missing data points were handled using appropriate imputation techniques or by excluding records with significant missing information.Usage: This dataset was utilized to train and test machine learning models, aiming to predict the market price of residential properties based on the provided attributes. The models developed using this dataset demonstrated improved accuracy and transparency over traditional appraisal methods.Dataset Availability: The dataset is available for public use under the [CC BY 4.0]. Users are encouraged to cite the related publication when using the data in their research or applications.Citation: If you use this dataset in your research, please cite the following publication:[Real Estate Decision-Making: Precision in Price Prediction through Advanced Machine Learning Algorithms].

Amsterdam is set to maintain its position as Europe's most expensive city for apartment rentals in 2025, with median costs reaching 2,500 euros per month for a furnished unit. This figure is double the rent in Prague and significantly higher than other major European capitals like Paris, Berlin, and Madrid. The stark difference in rental costs across European cities reflects broader economic trends, housing policies, and the complex interplay between supply and demand in urban centers. Factors driving rental costs across Europe The disparity in rental prices across European cities can be attributed to various factors. In countries like Switzerland, Germany, and Austria, a higher proportion of the population lives in rental housing. This trend contributes to increased demand and potentially higher living costs in these nations. Conversely, many Eastern and Southern European countries have homeownership rates exceeding 90 percent, which may help keep rental prices lower in those regions. Housing affordability and market dynamics The relationship between housing prices and rental rates varies significantly across Europe. As of 2024, countries like Turkey, Iceland, Portugal, and Hungary had the highest house price to rent ratio indices. This indicates a widening gap between property values and rental costs since 2015. The affordability of homeownership versus renting differs greatly among European nations, with some countries experiencing rapid increases in property values that outpace rental growth. These market dynamics influence rental costs and contribute to the diverse rental landscape observed across European cities.

In Toronto, the gross rental yields amounted to 3.8 percent in the city center as of January 2024, whereas rental yields outside the city center were higher at 4.12 percent. Rental yield is the amount a property investor is likely to earn through renting a property. It’s calculated by dividing the total amount invested into the property by the annual rental income of the property. Vancouver vs TorontoRental yields are very important to potential investors and are an important factor they look at when deciding where to invest. City center properties generally command higher rents than those outside the center, though not in this case. Vancouver has higher rents for both two-bedroom apartment and one-bedroom apartments. Vancouver rents increasingMonthly rents for residential units in Vancouver have steadily risen over the past decade. The share of rental units which were unoccupied was low across all unit sizes. This bodes well for rental yields in the market in the future.

Rents in Germany continued to increase in all seven major cities in 2024. The average rent per square meter in Munich was approximately **** euros — the highest in the country. Conversely, Düsseldorf had the most affordable rent, at approximately **** euros per square meter. But how does renting compare to buying? According to the house price to rent ratio, house prices in Germany have risen faster than rents, making renting more affordable than buying. Affordability of housing in Germany In 2023, Germany was among the European countries with a relatively high house price to income ratio in Europe. The indicator compares the affordability of housing across OECD countries and is calculated as the nominal house prices divided by nominal disposable income per head, with 2015 chosen as a base year. Between 2012 and 2022, property prices in the country rose much faster than income, with the house price to income index peaking at *** index points at the beginning of 2022. Slower house price growth in the following years has led to the index declining, as incomes catch up. Nevertheless, homebuyers in 2024 faced significantly higher mortgage interest rates, contributing to a higher final cost. How much does buying a property in Germany cost? Just as with renting, Munich was the most expensive city for newly built apartments. In 2024, the cost per square meter in Munich was almost ***** euros pricier than in the runner-up city, Frankfurt. Detached and semi-detached houses are usually more expensive. The price gap between Munich and the second most expensive city, Stuttgart, was nearly ***** euros per square meter.

The sixth arrondissement of Paris was the area with the highest residential real estate price in the French capital as of May 2025. In this arrondissement, which includes several historical sites like Saint-Germain-des-Prés, the Académie Française, and the Jardin du Luxembourg, the average price per square meter amounted to over ****** euros. Paris is known for being one of the most expensive European cities to rent an apartment. The price difference in the twenty arrondissements of Paris The French capital is divided into twenty arrondissements, which correspond to administrative districts. Because of their geographical situations in regards with the economic centers of the city of Paris, as well as their environments and the living conditions they offer, arrondissements do not have the same average price per square meter. For example, the average square meter price for an apartment in cosmopolitan districts like the 19th and the 20th arrondissements, located in the northeastern part of the city, amounted to around ***** euros, compared to close to ****** euros in Le Marais (4th arrondissement). Paris was by far the most expensive city in France, regardless of the location of the accommodation. In 2023, the average price per square meter for rental flats reached ** euros in Paris and ** euros in Marseille, France’s second-largest city. The rise in rental prices in European cities It appears cities in Europe have seen their rental prices increasing over the past years. In Germany, for instance, if Berlin used to be described as “poor but sexy” (to quote Berlin’s former mayor Klaus Wowereit), it appears that the German capital is not unaffected by the rise in rents. From 2016 to 2022, the average rent price of residential property in Berlin went from *** euros per square meter to **** euros five years later.

Abstract copyright UK Data Service and data collection copyright owner.A series of surveys were carried out to provide factual and detailed information on the performance of 6 local authorities in council house allocation, improvement grants, council mortgages and council house sales. The information was intended to support inter-authority comparisons, and to check on variability of policy and practice. The emphasis was on the extent to which housing need was being met and housing opportunities created. Main Topics: Attitudinal/Behavioural Questions (SN: 205) This dataset records information collected from the West Bromwich Waiting List. Type of list, length of application, applicant's marital and family situation, whether baby expected at application data, 'points' (total and detailed breakdown, e.g. size of family points, shared accommodation points). Period of residence/employment in West Bromwich County Borough, tenure, household size and type, bedrooms for applicant's family, use of separate living room, whether family separated by accommodation (length of time), other persons in dwelling, amenities, any personal disabilities, cleanliness. Type of dwelling recommended/allocated, number of bedrooms needed, area, offers made, rent/floor area allocated, rateable value allowed, age/grade choice and allocation, category of tenant, origin of letting, present location, location allocated, comparison of density of occupation (present and previous). Background Variables (SN: 205) Age, sex, ethnic origin, household status, place of residence, number of children less than/over 16 years of age, number under 5 years of age. Attitudinal/Behavioural Questions (SN: 263, 268, 271, 274, 277 and 280) Type of list, type of house, tenure, number of bedrooms, whether living room shared, other persons in house, standard of decorations. Type of house wanted, reasons for application, offers made, rent record. Expectant mother at application, medical claims 'points'. Required: type of dwelling, number of bedrooms, garage or car space. Location, age and grade of house (chosen and allocated). Present, chosen and allocated density of occupation. Floor space allocated. Background Variables (SN: 263, 268, 271, 274, 277 and 280) Age, marital status, place of birth, children 16 and under/5 and under, household size and type, length of residence at present address and in UK. Attitudinal/Behavioural Questions (SN: 264) Length of residence, whether on council waiting list, owner occupier, whether other property owned, present rent, rent willing to pay, general condition of property, cleanliness, rent record, medical problems, offers made, type of dwelling allocated, rent allocated, rateable value allocated, category of tenant, origin of letting, present, chosen and allocated location, age and grade of house, density of occupation allocated. Background Variables (SN: 264) Age, children 15 and under/5 and under, household type and size, number in employment, total income, car ownership. Attitudinal/Behavioural Questions (SN: 265) Size and age of house, mortgage intention, market price, sale price, % discount, market price above construction cost, length of tenancy, reasons for withdrawal, rent record, previous tenure, family size on application, whether still at same address, density of occupation, grade of estate, car parking facilities. Background Variables (SN: 265) Age, children 15 and 5 and under, household type. Attitudinal/Behavioural Questions (SN: 266) Term of loan sought, reference satisfactory, income satisfactory, price, loan sought, valuation, advance approved, balance of annual repayments, valuation as % price, loan granted as % price, loan approved as % valuation, loan approved as % price, time taken for approval, whether applicant is tenant, whether part of house would be let in future, freehold or leasehold, rateable value, notices to repair outstanding, type of property, number of bedrooms, garden, garage, hot water system, age of buildings, annual basic earnings, overtime, total earnings, total household income, annual repayment as % applicant's annual earnings, annual repayments as % household annual earnings, mortgage held. Background Variables (SN: 266) Age, place of birth, family size, social class. Variables (SN: 267, 270, 273, 276 and 279) Type of grant, nature of work, cost approved, maximum grant, age of property, tenure, mortgage, cost of improvement, cost of repairs as % approved costs, grant as % total costs, total cost of work, grant approved, date of application, time taken from application to approval, time taken from approval to completion, time taken from application to completion, area, house type. Attitudinal/Behavioural Questions (SN: 269, 272, 275, 278 and 281) Period of loan sought, income status, period of loan granted, category of tenant, price, loan applied for, valuation, advance given, balance, annual repayments, valuation as % price, loan granted as % loan sought, loan as % price, loan as % valuation, time taken from application to approval. Length of tenancy, rate of interest, earnings, overtime, other earnings, total applicant's earnings, total household income, previous rent, repayments as % previous rent. Whether applicant is tenant, whether part of house would be let in future, freehold or leasehold, rateable value, repairs required, type of house, garden, garage, hot water system, central heating, number of bedrooms, age of property, mortgage, area, grade of estate, previous tenure, density of occupation. Background Variables (SN: 269, 272, 275, 278 and 281) Age, social class, children 16 and under/5 and under, household type and size.