The records in this dataset show the listings UNDER 750euros/month, for Madrid province.

The dataset contains 40 columns, all regarding every of the listed properties in Idealista.

This is all the data that got returned from the API, so it can be concatenated to new datasets obtained from the same API, without doing modifications.

Average rental price in EUR/m² per neighborhood of the city of Valencia according to the Idealist sales portal.

Housing Index in France decreased to 126.30 points in the second quarter of 2025 from 126.54 points in the first quarter of 2025. This dataset provides the latest reported value for - France House Price Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

🏠 Madrid Idealista Property Listings 🇪🇸

Overview

This dataset provides a comprehensive collection of property listings from Madrid, Spain, sourced from the popular real estate platform, Idealista. It includes a variety of details for each listing, such as the URL, title, ID, price, number of bathrooms and rooms, square footage, description, address, typology, and the name of the advertiser.

Data Science Applications

This dataset is a valuable resource for data science projects and can be used for various applications such as: - Real Estate Market Analysis: Understand the housing market in Madrid, identify trends, and make predictions. - Price Prediction Models: Develop machine learning models to predict property prices based on various features. - Geospatial Analysis: Analyze the geographical distribution of properties and their characteristics.

Column Descriptors

• url: The URL of the property listing.
• listingUrl: The URL of the page where the listing is found.
• title: The title of the property listing.
• id: The unique identifier for the property.
• price: The listed price of the property.
• baths: The number of bathrooms in the property.
• rooms: The number of rooms in the property.
• sqft: The square footage of the property.
• description: A description of the property.
• address: The address of the property.
• typology: The type of property (e.g., Pisos).
• advertiserProfessionalName: The professional name of the advertiser.
• advertiserName: The name of the advertiser.

Ethically Mined Data

This dataset has been ethically mined, using an API and only Publicly Available Data.

Acknowledgements

We would like to express our gratitude to Idealista for providing the platform from which this data was sourced. Their commitment to making real estate data accessible contributes significantly to the richness of this dataset.

Please note that this dataset is intended for educational and research purposes only. Always respect the terms of use of the source website when using this data. Happy data crunching! 📊

Average price of the house for sale in EUR/m² per neighborhood of the city of Valencia according to the Idealist sales portal

This research data file contains the necessary software and the dataset for estimating the missing prices of house units. This approach combines several machine learning techniques (linear regression, support vector regression, the k-nearest neighbors and a multi-layer perceptron neural network) with several dimensionality reduction techniques (non-negative factorization, recursive feature elimination and feature selection with a variance threshold). It includes the input dataset formed with the available house prices in two neighborhoods of Teruel city (Spain) in November 13, 2017 from Idealista website. These two neighborhoods are the center of the city and “Ensanche”.

This dataset supports the research of the authors in the improvement of the setup of agent-based simulations about real-estate market. The work about this dataset has been submitted for consideration for publication to a scientific journal.

The open source python code is composed of all the files with the “.py” extension. The main program can be executed from the “main.py” file. The “boxplotErrors.eps” is a chart generated from the execution of the code, and compares the results of the different combinations of machine learning techniques and dimensionality reduction methods.

The dataset is in the “data” folder. The input raw data of the house prices are in the “dataRaw.csv” file. These were shuffled into the “dataShuffled.csv” file. We used cross-validation to obtain the estimations of house prices. The outputted estimations alongside the real values are stored in different files of the “data” folder, in which each filename is composed by the machine learning technique abbreviation and the dimensionality reduction method abbreviation.

In this project, I extracted data from Idealista, a real estate website, and performed a thorough cleaning process to ensure data accuracy. Following this, I conducted an exploratory data analysis (EDA) to uncover trends and patterns in the real estate market. The main objectives were to understand market trends, identify factors influencing property prices, and provide insights for buyers, sellers, and investors.

This project is currently under development and I'll be updating it with new notebooks.

1. Raw_data

• id: Property identificator
• title: Property type and address
• neighborhood: District and city
• price: Price in euros
• info_features: List of property features like surface, bedrooms amount, floor number, etc.
• features: A more extended list of property features like surface, bedrooms amount, floor number, etc.
• description: Text description of the property

2. Structured_dataw_description

• id: Property identificator
• property_type: Type of property
• adress: Approximated adress of property
• neighborhood: Neighborhood name
• city: City name
• price: Price in euros
• sq_mt_built: Area of the property in square meters with walls
• sq_mt_floor_area: Usable area of the property in square meters
• n_bedrooms: Bedrooms amount
• bathrooms: Bathrooms amount
• floor_y: Floor number
• year_built: Year of building construction
• exterior_x: Is the property exterior? (True/False) -**lift_x**: Has the property a lift? (True/False)
• terrace: Has the property a terrace? (True/False)
• balconyHas the property a balcony? (True/False)
• second hand: Is the property second hand? (True/False)
• needs renovating: Does the property need renovating? (True/False)
• parking: Has the property a parking slot? (True/False)
• swimming pool: Has the property a swimming pool? (True/False)
• garden: Has the property a garden? (True/False)
• air conditioning: Has the property air conditioning? (True/False)
• heating: Has the property a heating system? (True/False)
• central_heating: Is the heating system cental/individual?
• heating_type: Type of heating
• consumption_in_mkw/m2_year: Yearly consumtion of the property in mkW/m^2
• emissions_in_kgco2/m2_year: Yearly emissions of the property in kgco2/m^2
• orientation: Orientation of the property
• description: Text description of the property

This data was scraped in April 2024

This dataset has been extracted as a form to identify what's the percentage of listings in Spain. It contains the price data for all the provinces and some listing details.

The full code to extract data using Beautiful Soup from Idealista can be found in my GitHub repository: https://github.com/laurabarredaagusti/idealista_data_extraction

This dataset contains the property listings in Madrid as of October 31st, 2021

From each listing, we got the following fields:

ID: the listing ID Listing: It shows the listing it usually contains the type of property and the street. Location: the place of the listing (city, town or neighborhood) price: property price old_price: previous price before the discount. discount: percentage of discount meters: size of the property in meters sq_meter_price: price of each square meter. rooms: number of rooms of the property floor: the floor of the property garage: It shows if the property has a garage description: This field is the description of the property in Spanish.

All the data has been scraped from idealista containing the listings as of October 31st, 2021 in Madrid. The process took about 30 hours.

Spanish:

El dataset contiene los inmuebles listados para su venta en la Comunidad de Madrid a fecha 31 de octubre de 2021.

De cada una de las ofertas de venta se han recogido los siguientes campos:

ID: Es el identificador del anuncio Listing: Contiene el título del anuncio que normalmente se compone del tipo de vivienda y de la calle en la que se encuentra. Location: es la ubicación del inmueble que puede ser la ciudad, el pueblo o el barrio.
price: precio de venta del inmueble. old_price: campo solo disponible en los inmuebles rebajados e indica el precio anterior del inmueble discount: porcentaje de descuento del precio. meters: metros cuadrados de la vivienda sq_meter_price: Precio del metro cuadrado del inmueble rooms: Número de habitaciones del inmueble floor: Planta del inmueble garage: campo que indica si el inmueble dispone de garaje, cuando su valor es nulo indica que no aparece reflejado que disponga de garaje. description: Es un campo abierto donde aparece la descripción del inmueble en venta.

Los datos han sido extraídos del portal inmobiliario idealista y el dataset contiene los inmuebles listados a fecha 31/10/2021 en la Comunidad de Madrid. Ha sido obtenido mediante un web scraper desarrollado en python en un proceso que ha durado unas 30 horas.

Estimation of the average sale price of second-hand homes (€/m2) of the Idealista.com portal for each of the neighborhoods of the city of Barcelona.

Real estate market metrics for Properties Transacted

Dataset that contains data scraped from the websites of Fotocasa and Idealista between 4^th and 7^th April 2022 and it is meant only for academic purposes.

Each record describes a house for sale in the Salamanca and Villaverde districts of Madrid by the following fields: id, url, title, location, price, m2, rooms, floor, num-photos, floor-plan, view3d, video, home-staging, description, photo_urls and source.

The context of this project is the Data Science Master’s Degree of UOC (Universitat Oberta de Catalunya), specifically the subject Data Typology and Life Cycle’.

Real estate market metrics for LMA Average Price per m²

Context

This dataset compiles data regarding housing prices in Barcelona. The houses are randomly selected from the 10 Barcelona Districts. The data has been compiled using web scrapping. Data gathered in 2020/12/10.

Average price per m² across all 25 LMA municipalities (historical analysis + current research)

Dataset with properties to rent in Madrid province from Idealista (https://www.idealista.com/en/alquiler-viviendas/madrid-provincia/). The data was scraped at 2022-11-08.

Contact: info@propertdata.net

Real estate market metrics for Price Growth Expectations

Context

Based in the Boston Housing dataset, I was wondering to create a similar amount of fresh houses for sale based on real (and current) information from my home city, Seville (Spain).

Content

This dataset contains 1844 houses announced in May 2020. Please, note that some of the houses might be duplicated, as sometimes the seller publishes the same house twice with a slightly different amount. An interesting idea to clean the data before using would be to check if some of the fields or the whole row appeared before.

Some details about the fields included:

• The 'title' field contains a first word explaining if the row describes details for an appartment (piso) or a House (casa). This can be useful to get a new information column.

• The column 'price', must be multiplied by *1000 and is in € (euros), the currency currently used in Spain.

• The year of construction was not included in all of the adverts, so the ones without this information field were marked as 0.

• There was no public information related to criminality statistics, so I came up with the idea that the neighbourhoods with less hotels would be the most dangerous ones (or at least the less attractive). The number of hotels for each neighbourhood was extracted from http://www.hotelsearch.com/

• A house includes garage when the field value is set to 1. The same idea has been applied to the 'terrace' field.

Acknowledgements

All these houses information has been extracted with data scraping techniques from Idealista website.

Comprehensive comparison of real estate prices and market data across Lisbon neighborhoods

Real estate market metrics for Average Time on Market