Graph and download economic data for Average Sales Price of Houses Sold for the United States (ASPUS) from Q1 1963 to Q2 2025 about sales, housing, and USA.

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Same dataset as "House Sales in King County, USA", but with treated content and with a split version (train-test) allowing direct use in machine learning models.

We have 14 columns in the dataset, as it follows:

• date: Date of the home sale
• price: Price of each home sold
• bedrooms: Number of bedrooms
• bathrooms: Number of bathrooms
• living_in_m2: Square meters of the apartments interior living space
• nice_view: A flag that indicates the view's quality of a property
• perfect_condition: A flag that indicates the maximum index of the apartment condition
• grade: An index from 1 to 5, where 1 falls short of quality level and 5 have a high quality level of construction and design
• has_basement: A flag indicating whether or not a property has a basement
• renovated: A flag if the property was renovated
• has_lavatory: Check for the presence of these incomplete/secondary bathrooms (bathtub, sink, toilet)
• single_floor: A flag indicating whether the property had only one floor
• month: The month of the home sale
• quartile_zone: A quartile distribution index of the most expensive zip codes, where 1 means less expansive and 4 most expansive.

This dataset contains data on all Real Property parcels that have sold since 2013 in Allegheny County, PA.

Before doing any market analysis on property sales, check the sales validation codes. Many property "sales" are not considered a valid representation of the true market value of the property. For example, when multiple lots are together on one deed with one price they are generally coded as invalid ("H") because the sale price for each parcel ID number indicates the total price paid for a group of parcels, not just for one parcel. See the Sales Validation Codes Dictionary for a complete explanation of valid and invalid sale codes.

Sales Transactions Disclaimer: Sales information is provided from the Allegheny County Department of Administrative Services, Real Estate Division. Content and validation codes are subject to change. Please review the Data Dictionary for details on included fields before each use. Property owners are not required by law to record a deed at the time of sale. Consequently the assessment system may not contain a complete sales history for every property and every sale. You may do a deed search at http://www.alleghenycounty.us/re/index.aspx directly for the most updated information. Note: Ordinance 3478-07 prohibits public access to search assessment records by owner name. It was signed by the Chief Executive in 2007.

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 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.

Field Name	Description	Type
PropertyID	A unique identifier for each property.	text
PropType	The type of property (e.g., Commercial or Residential).	text
taxkey	The tax key associated with the property.	text
Address	The address of the property.	text
CondoProject	Information about whether the property is part of a condominium	text
	project (NaN indicates missing data).
District	The district number for the property.	text
nbhd	The neighborhood number for the property.	text
Style	The architectural style of the property.	text
Extwall	The type of exterior wall material used.	text
Stories	The number of stories in the building.	text
Year_Built	The year the property was built.	text
Rooms	The number of rooms in the property.	text
FinishedSqft	The total square footage of finished space in the property.	text
Units	The number of units in the property	text
	(e.g., apartments in a multifamily building).
Bdrms	The number of bedrooms in the property.	text
Fbath	The number of full bathrooms in the property.	text
Hbath	The number of half bathrooms in the property.	text
Lotsize	The size of the lot associated with the property.	text
Sale_date	The date when the property was sold.	text
Sale_price	The sale price of the property.	text

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].

Reference: https://www.zillow.com/research/zhvi-methodology/

Official Background

In setting out to create a new home price index, a major problem Zillow sought to overcome in existing indices was their inability to deal with the changing composition of properties sold in one time period versus another time period. Both a median sale price index and a repeat sales index are vulnerable to such biases (see the analysis here for an example of how influential the bias can be). For example, if expensive homes sell at a disproportionately higher rate than less expensive homes in one time period, a median sale price index will characterize this market as experiencing price appreciation relative to the prior period of time even if the true value of homes is unchanged between the two periods.

The ideal home price index would be based off sale prices for the same set of homes in each time period so there was never an issue of the sales mix being different across periods. This approach of using a constant basket of goods is widely used, common examples being a commodity price index and a consumer price index. Unfortunately, unlike commodities and consumer goods, for which we can observe prices in all time periods, we can’t observe prices on the same set of homes in all time periods because not all homes are sold in every time period.

The innovation that Zillow developed in 2005 was a way of approximating this ideal home price index by leveraging the valuations Zillow creates on all homes (called Zestimates). Instead of actual sale prices on every home, the index is created from estimated sale prices on every home. While there is some estimation error associated with each estimated sale price (which we report here), this error is just as likely to be above the actual sale price of a home as below (in statistical terms, this is referred to as minimal systematic error). Because of this fact, the distribution of actual sale prices for homes sold in a given time period looks very similar to the distribution of estimated sale prices for this same set of homes. But, importantly, Zillow has estimated sale prices not just for the homes that sold, but for all homes even if they didn’t sell in that time period. From this data, a comprehensive and robust benchmark of home value trends can be computed which is immune to the changing mix of properties that sell in different periods of time (see Dorsey et al. (2010) for another recent discussion of this approach).

For an in-depth comparison of the Zillow Home Value Index to the Case Shiller Home Price Index, please refer to the Zillow Home Value Index Comparison to Case-Shiller

Each Zillow Home Value Index (ZHVI) is a time series tracking the monthly median home value in a particular geographical region. In general, each ZHVI time series begins in April 1996. We generate the ZHVI at seven geographic levels: neighborhood, ZIP code, city, congressional district, county, metropolitan area, state and the nation.

Underlying Data

Estimated sale prices (Zestimates) are computed based on proprietary statistical and machine learning models. These models begin the estimation process by subdividing all of the homes in United States into micro-regions, or subsets of homes either near one another or similar in physical attributes to one another. Within each micro-region, the models observe recent sale transactions and learn the relative contribution of various home attributes in predicting the sale price. These home attributes include physical facts about the home and land, prior sale transactions, tax assessment information and geographic location. Based on the patterns learned, these models can then estimate sale prices on homes that have not yet sold.

The sale transactions from which the models learn patterns include all full-value, arms-length sales that are not foreclosure resales. The purpose of the Zestimate is to give consumers an indication of the fair value of a home under the assumption that it is sold as a conventional, non-foreclosure sale. Similarly, the purpose of the Zillow Home Value Index is to give consumers insight into the home value trends for homes that are not being sold out of foreclosure status. Zillow research indicates that homes sold as foreclosures have typical discounts relative to non-foreclosure sales of between 20 and 40 percent, depending on the foreclosure saturation of the market. This is not to say that the Zestimate is not influenced by foreclosure resales. Zestimates are, in fact, influenced by foreclosure sales, but the pathway of this influence is through the downward pressure foreclosure sales put on non-foreclosure sale prices. It is the price signal observed in the latter that we are attempting to measure and, in turn, predict with the Zestimate.

Market Segments Within each region, we calculate the ZHVI for various subsets of homes (or mar...

The house price index in London reached 99.1 index points in May 2025, which was an increase of 2.2 percent year on year. The house price index (HPI) is an easy way of illustrating trends in the house sales market and help simplify house purchase decisions. By using hedonic regression, the index models property price data for all dwellings and shows how much the price has changed since January 2023. Average house prices in Londnon boroughs Location plays a huge role in the price of a home. Kensington and Chelsea and City of Westminster are undoubtedly the most expensive boroughs in London, with an average house price that can exceed one million British pounds. In comparison, a house in Barking and Dagenham cost approximately one third. Nevertheless, the housing market is the busiest in the boroughs with average house prices. How have regional house prices in the UK developed? House prices in other UK regions have risen even more than in London. In Northern Ireland, the house price index reached nearly 120 index points in May 2025, ranking it among the regions with the highest property appreciation. The UK house price index stood at 103 index points, suggesting an increase of 51 percent since 2015.

Quarterly median house prices for metropolitan Adelaide by suburb

Context

Real estate prices in Seoul are skyrocketing day by day. This may be unfortunate for someone, but it can also be a huge benefit for someone. Can you be the owner of the profit? Check it out! (Only apartment)

Content

id : Primary key for a specific apartment lat: Latitude lng: Longitude households: Number of households in residence buildDate: Date the apartment was built score: Total evaluation, maximum 5 stars m2: The area of a house(m^2) p: Number of floors min_sales, max_sales, avg_sales: Descriptive statistics of sales price

This dataset contains detailed information on current real estate listings in Houston, Texas, sourced from Zillow, and provides a comprehensive snapshot of the Houston housing market as of 5th June 2024.

The data was extracted from Zillow using a combination of two scraping tools from Apify: Zillow ZIP Code Scraper 🔗 https://apify.com/maxcopell/zillow-zip-search and Zillow Details Scraper 🔗 https://apify.com/maxcopell/zillow-detail-scraper.

The data includes key details for each listing for sale, such as:

• 📍 Complete address, city, state, zip code, latitude/longitude coordinates
• 🏡 Property type (single family, condo, apartment, etc.)
• 💵 Listing price
• 🛏️ Number of bedrooms and bathrooms
• 📐 Square footage
• 🌳 Lot size in acres (if applicable)
• 🏗️ Year of construction
• 🏘️ HOA fees (if applicable)
• 💸 Property tax history
• ✨ Amenities such as rooftop terraces, concierge services, etc.
• 🏫 Nearby schools and their GreatSchools ratings
• 🧑‍💼 Property and listing agents, brokers, and their contact information
• 🕒 Availability for tours and open houses
• 🖼️ Links to listing photos

With 25,900 current listings, this dataset is ideal for in-depth analysis of the Houston housing market and the Houston real estate market. Potential use cases include:

• Comparing listing prices, price per square foot across different neighborhoods, property types
• Mapping listings to visualize the spatial distribution of for-sale inventory
• Analyzing the age of for-sale housing stock from year-built data
• Evaluating typical HOA fees, and property taxes for listings
• Identifying listings with sought-after amenities
• Assessing school quality near listings from GreatSchools ratings
• Contacting listing agents programmatically using the included agent info

Whether you're a real estate professional, market researcher, data scientist, or just curious about the Houston housing market, this dataset provides a wealth of information to explore. You can start investigating Houston real estate today.

The Easiest Way to Collect Data from the Internet Download anything you see on the internet into spreadsheets within a few clicks using our ready-made web crawlers or a few lines of code using our APIs

We have made it as simple as possible to collect data from websites

Easy to Use Crawlers Amazon Product Details and Pricing Scraper Amazon Product Details and Pricing Scraper Get product information, pricing, FBA, best seller rank, and much more from Amazon.

Google Maps Search Results Google Maps Search Results Get details like place name, phone number, address, website, ratings, and open hours from Google Maps or Google Places search results.

Twitter Scraper Twitter Scraper Get tweets, Twitter handle, content, number of replies, number of retweets, and more. All you need to provide is a URL to a profile, hashtag, or an advance search URL from Twitter.

Amazon Product Reviews and Ratings Amazon Product Reviews and Ratings Get customer reviews for any product on Amazon and get details like product name, brand, reviews and ratings, and more from Amazon.

Google Reviews Scraper Google Reviews Scraper Scrape Google reviews and get details like business or location name, address, review, ratings, and more for business and places.

Walmart Product Details & Pricing Walmart Product Details & Pricing Get the product name, pricing, number of ratings, reviews, product images, URL other product-related data from Walmart.

Amazon Search Results Scraper Amazon Search Results Scraper Get product search rank, pricing, availability, best seller rank, and much more from Amazon.

Amazon Best Sellers Amazon Best Sellers Get the bestseller rank, product name, pricing, number of ratings, rating, product images, and more from any Amazon Bestseller List.

Google Search Scraper Google Search Scraper Scrape Google search results and get details like search rank, paid and organic results, knowledge graph, related search results, and more.

Walmart Product Reviews & Ratings Walmart Product Reviews & Ratings Get customer reviews for any product on Walmart.com and get details like product name, brand, reviews, and ratings.

Scrape Emails and Contact Details Scrape Emails and Contact Details Get emails, addresses, contact numbers, social media links from any website.

Walmart Search Results Scraper Walmart Search Results Scraper Get Product details such as pricing, availability, reviews, ratings, and more from Walmart search results and categories.

Glassdoor Job Listings Glassdoor Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Glassdoor.

Indeed Job Listings Indeed Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Indeed.

LinkedIn Jobs Scraper Premium LinkedIn Jobs Scraper Scrape job listings on LinkedIn and extract job details such as job title, job description, location, company name, number of reviews, and more.

Redfin Scraper Premium Redfin Scraper Scrape real estate listings from Redfin. Extract property details such as address, price, mortgage, redfin estimate, broker name and more.

Yelp Business Details Scraper Yelp Business Details Scraper Scrape business details from Yelp such as phone number, address, website, and more from Yelp search and business details page.

Zillow Scraper Premium Zillow Scraper Scrape real estate listings from Zillow. Extract property details such as address, price, Broker, broker name and more.

Amazon product offers and third party sellers Amazon product offers and third party sellers Get product pricing, delivery details, FBA, seller details, and much more from the Amazon offer listing page.

Realtor Scraper Premium Realtor Scraper Scrape real estate listings from Realtor.com. Extract property details such as Address, Price, Area, Broker and more.

Target Product Details & Pricing Target Product Details & Pricing Get product details from search results and category pages such as pricing, availability, rating, reviews, and 20+ data points from Target.

Trulia Scraper Premium Trulia Scraper Scrape real estate listings from Trulia. Extract property details such as Address, Price, Area, Mortgage and more.

Amazon Customer FAQs Amazon Customer FAQs Get FAQs for any product on Amazon and get details like the question, answer, answered user name, and more.

Yellow Pages Scraper Yellow Pages Scraper Get details like business name, phone number, address, website, ratings, and more from Yellow Pages search results.

Property currently or historically owned and managed by the City of Chicago. Information provided in the database, or on the City’s website generally, should not be used as a substitute for title research, title evidence, title insurance, real estate tax exemption or payment status, environmental or geotechnical due diligence, or as a substitute for legal, accounting, real estate, business, tax or other professional advice. The City assumes no liability for any damages or loss of any kind that might arise from the reliance upon, use of, misuse of, or the inability to use the database or the City’s web site and the materials contained on the website. The City also assumes no liability for improper or incorrect use of materials or information contained on its website. All materials that appear in the database or on the City’s web site are distributed and transmitted "as is," without warranties of any kind, either express or implied as to the accuracy, reliability or completeness of any information, and subject to the terms and conditions stated in this disclaimer.

The following columns were added 4/14/2023:

• Sales Status
• Sale Offering Status
• Sale Offering Reason
• Square Footage - City Estimate
• Land Value (2022) -- Note: The year will change over time.

The following columns were added 3/19/2024:

• Application Use
• Grouped Parcels
• Application Deadline
• Offer Round
• Application URL

Housing Prices and Access to Cannabis

A comparison of legal and non-legal states

About this dataset

The legal cannabis industry is booming, and it's having a major impact on housing prices across the country. In states where recreational cannabis sales are legal, prices have skyrocketed. But what about in states where it is not?

This dataset compare housing prices in legal and non-legal states, in order to determine whether or not there is a correlation between the two. Are houses in legal states really worth more? Or is something else at play?

Download the dataset and take a closer look to find out!

• State: The state in which the housing data was collected
• HPI_AT_BDL_national: The national average home price index (HPI) value for that state
• HPI_AT_BDL_state: The statewide home price index (HPI) value for that state
• legal: A binary value indicating whether or not recreational cannabis sales are legal in that state

Research Ideas

• Research whether or not there is a correlation between housing prices and states where recreational cannabis sales are legal.
• Compare housing prices in states where recreational cannabis sales are legal and those where they are not, in order to determine if there is a difference between the two groups.
• Examine if there is a relationship between changes in housing prices and changes in the legal status of recreational cannabis sales in a state

Do you want to explore and predict real estate data of the biggest city of south emisphere, 4th largest in the world? Sao Paulo - Brazil has over 14,000 official real state transactions per month. This dataset shows REAL transactions and values registered in the city hall (it is not advertising scrapping). That means you will be dealing with real market values, aside of expeculations. You can predict property prices, check the most valued or devalued districts, look for features that affect prices the most, find trends on the different type of properties and much more. The data is quite recent, from May/22 to Oct/22.

Column descriptions: tax_id: tax id at the city hall registers street_name: street name where the property is located street_number: property street number complement: complement like apartment number, block or tower in an apartment building, etc. district: zone or district in the Sao Paulo city, where the property is located reference: general reference of the property zip_code: zip code transaction_nature: legal motivation for that transaction like a simple buy/sell, or a transmission of rights, person-company transferences, etc. transaction_value_BRL: real value of the transaction in BRL (Brazilian Reais) date: date of the transaction cadastral_value: property value in the city hall registers in BRL (Brazilian Reais) tax_base_value: base value for transaction tax calculation in BRL (Brazilian Reais) mortgage_type: mortgage type, if any mortgage_value: value in BRL (Brazilian Reais) of the mortgage registry_number: real estate registration office id property_id: property id in the real estate registration offices city_hall_status: status of the property according to the city hall land_area_m2: area of the property in squared meters (m2) front_length_m: front length of the property, facing the street in meters (m) ideal_fraction: fraction of the total property transactioned area_built_m2: property built area in squared meters (m2) description_1: occupation description description_2: type of property year_built: year of the construction conclusion

Metadata The ‘Requests for land values’ database, or DVF, lists all sales of land over the last five years, in mainland France and in the overseas departments and territories — except in Mayotte and Alsace-Moselle. The properties concerned can be built (apartment and house) or unbuilt (plots and farms). The data are produced by Bercy, i.e. by the Directorate-General for Public Finance. They come from the deeds registered with notaries and the information contained in the cadastre. Legal framework: The DVF database does not contain personal data, such as the name of the seller or the buyer of a good. It contains only information on transactions: type of property sold, area, selling price and so on. As these data can be cross-checked with other data already online, the Directorate-General for Public Finance recalls that the use of data from the DVF database cannot have the purpose or effect of allowing the re-identification of data subjects, nor should it be indexed on online search engines. Consult the general conditions of use: https://static.data.gouv.fr/resources/request-de-valeurs-foncieres/20190419-091643/conditions-generales-dutilisation.pdf Fields code_service_ch: not provided reference_document: not entered articles_cgi1: not entered articles_cgi2: not entered articles_cgi3: not entered articles_cgi4: not entered articles_cgi5: No_provision: Each provision of a document has a number. Only the provisions concerning transfers for consideration are returned to the file. The provisions concerning transfers free of charge are removed from the register by the application. The disposition numbers used do not therefore necessarily follow the numerical order date_mutation: Date of signature of nature_mutation document: Sale, sale in the future state of completion, sale of building land, tendering, expropriation or exchange of land value: This is the price or valuation declared in the context of a transfer for consideration. It can correspond to several properties. The details are not traced in the information system no_voie: Number in track b_t_q: Repetition index type_of_way: Track type (example: Street, Avenue,...) code_voie: Track code: Wording of the code_postal route: Common postal code: Wording of the commune code_departement: Common_code department code: Common code prefix_of_section: Prefix of cadastral section section: Cadastral section no_plan: Cadastral plan no_volume: Cadastral volume A condominium lot consists of a private part (apartment, cellar, etc.) and a share of the common part (tenths). Only the first 5 lots are mentioned. If the number of lots exceeds 5, they will not be returned. 1st lot surface_carrez_du_1er_lot: surface area CARREZ of the 1st lot 2nd_lot: 2nd lot surface_carrez_du_2eme_lot: surface area CARREZ of the 2nd lot 3rd_lot: 3rd lot surface_carrez_du_3eme_lot: CARREZ surface area of the third lot, fourth lot: 4th lot surface_carrez_du_4eme_lot: surface area CARREZ of the 4th lot 5th_lot: 5th lot surface_carrez_du_5eme_lot: surface area CARREZ of the 5th lot number_of_lots: Total number of lots per layout code_type_local: Local type code type_local 1: House, 2: apartment, 3: dependency (isolated), 4: Industrial and commercial premises or similar identifier_local: This is the number that identifies each room. The local is a tax concept of built property. The file includes one line per number (per local) with the corresponding real area surface_reelle_bati next to it: The real area is attached to the local identifier. This is the sum of the actual surface area of the premises and the surface areas of the outbuildings (see real estate lexicon) number_pieces_principal: Number of main nature_culture parts: Nature of culture nature_culture_speciale: Nature of special crop surface_terrain: Building land capacity: indicates the presence of racks (non-zero local_type) nb_line: Number of lines of the transaction (number of lines on grouping of a single value of the department code set, common code, date of transfer, nature of transfer, land value, no_disposition) id_parcelle: PCI-type parcel identifier

This dataset contains apartment sale listings scraped from three major real estate platforms in Tunisia:

• Tayara.tn
• Menzili.tn
• Mubawab.tn

Using Scrapy, we collected details such as location, area, number of rooms, floor level, and price. The data is cleaned and preprocessed, making it ideal for:

Predicting Apartment Prices
Real Estate Market Trends Analysis
Machine Learning Model Training

File Information

File Name	Description
menzili_preprocessed.csv	Cleaned dataset from Menzili.tn
mubawab_preprocessed.csv	Cleaned dataset from Mubawab.tn
tayara_preprocessed.csv	Cleaned dataset from Tayara.tn
mubawab_tayara_menzili_final.csv	Final merged dataset (Recommended for use)

Features

Feature	Description
delegation	Locality where the apartment is located
superficie	Total apartment area (sq meters)
chambres	Number of rooms
salle_de_bains	Number of bathrooms
etat	Apartment condition (new, renovated, old)
etage	Floor number
gouvernorat_ariana	1 if located in Ariana, 0 otherwise
gouvernorat_ben-arous	1 if located in Ben Arous, 0 otherwise
gouvernorat_la-manouba	1 if located in La Manouba, 0 otherwise
gouvernorat_tunis	1 if located in Tunis, 0 otherwise
prix	Apartment sale price (target variable)

Why Use This Dataset?

✔️ Real-world real estate data ✔️ Preprocessed & ready for ML models ✔️ Great for price prediction & market analysis

How to Use

Please refer to the src code of models in my GitHub repositpry

https://github.com/Chebil-Ilef/Apartment-Price-Predictor

💡 If you find this dataset useful, please upvote & share!

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,...

Geneva stands out as Europe's most expensive city for apartment purchases in early 2025, with prices reaching a staggering 15,720 euros per square meter. This Swiss city's real estate market dwarfs even high-cost locations like Zurich and London, highlighting the extreme disparities in housing affordability across the continent. The stark contrast between Geneva and more affordable cities like Nantes, France, where the price was 3,700 euros per square meter, underscores the complex factors influencing urban property markets in Europe. Rental market dynamics and affordability challenges While purchase prices vary widely, rental markets across Europe also show significant differences. London maintained its position as the continent's priciest city for apartment rentals in 2023, with the average monthly costs for a rental apartment amounting to 36.1 euros per square meter. This figure is double the rent in Lisbon, Portugal or Madrid, Spain, and substantially higher than in other major capitals like Paris and Berlin. The disparity in rental costs reflects broader economic trends, housing policies, and the intricate balance of supply and demand in urban centers. Economic factors influencing housing costs The European housing market is influenced by various economic factors, including inflation and energy costs. As of April 2025, the European Union's inflation rate stood at 2.4 percent, with significant variations among member states. Romania experienced the highest inflation at 4.9 percent, while France and Cyprus maintained lower rates. These economic pressures, coupled with rising energy costs, contribute to the overall cost of living and housing affordability across Europe. The volatility in electricity prices, particularly in countries like Italy where rates are projected to reach 153.83 euros per megawatt hour by February 2025, further impacts housing-related expenses for both homeowners and renters.

November 2025