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Description:

A simple yet challenging project, to predict the housing price based on certain factors like house area, bedrooms, furnished, nearness to mainroad, etc. The dataset is small yet, it's complexity arises due to the fact that it has strong multicollinearity. Can you overcome these obstacles & build a decent predictive model?

Acknowledgement:

Harrison, D. and Rubinfeld, D.L. (1978) Hedonic prices and the demand for clean air. J. Environ. Economics and Management 5, 81–102. Belsley D.A., Kuh, E. and Welsch, R.E. (1980) Regression Diagnostics. Identifying Influential Data and Sources of Collinearity. New York: Wiley.

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t a single & multiple feature.
• Also evaluate the models & compare thier respective scores like R2, RMSE, etc.

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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 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].

Quarterly house price data based on a sub-sample of the Regulated Mortgage Survey.

This dataset provides comprehensive information about rental house prices across various locations in India. It includes details such as house type, size, location, city, latitude, longitude, price, currency, number of bathrooms, number of balconies, negotiability of price, price per square foot, verification date, description of the property, security deposit, and status of furnishing (furnished, unfurnished, semi-furnished).

Note: This is Recently scraped data of April 2024.

Dataset Glossary (Column-Wise)

• House Type: Type of house (e.g., apartment, villa, duplex).
• House Size: Size of the house in square feet or square meters.
• Location: Specific area or neighborhood where the property is located.
• City: City in India where the property is situated.
• Latitude: Geographic latitude coordinates of the property location.
• Longitude: Geographic longitude coordinates of the property location.
• Price: Rental price of the house.
• Currency: Currency in which the price is denoted (e.g., INR - Indian Rupees).
• Number of Bathrooms: Total number of bathrooms in the house.
• Number of Balconies: Total number of balconies in the house.
• Negotiability: Indicates whether the price is negotiable (Yes/No).
• Price per Square Foot: Price of the house per square foot.
• Verification Date: Date when the rental information was verified.
• Description: Additional description or details about the property.
• Security Deposit: Amount of security deposit required for renting the property.
• Status: Indicates the furnishing status of the property (furnished, unfurnished, semi-furnished).

Usage

This dataset aims to provide valuable insights into the rental housing market in India, enabling analysis of rental trends, comparison of prices across different locations and property types, and understanding the impact of various factors on rental prices. Researchers, analysts, and policymakers can utilize this dataset for a wide range of applications, including real estate market analysis, urban planning, and economic research.

Acknowledgement

This Dataset is created from https://www.makaan.com/. If you want to learn more, you can visit the Website.

Cover Photo by: Playground.ai

Dataset

This dataset was created by Nagwa Ahmed

Released under Apache 2.0

Contents

Dataset

This dataset was created by NancyMee

Contents

Quarterly median house prices for metropolitan Adelaide by suburb

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

House price prediction dataset

This dataset comprises housing data for various metropolitan cities of India. It includes: - Collection of prices of new and resale houses - The amenities provided for each house

This housing dataset is useful for a range of stakeholders, including real estate agents, property developers, buyers, renters, and researchers interested in analyzing housing markets and trends in metropolitan cities across India. It can be used for market analysis, price prediction, property recommendations, and various other real estate-related tasks.

Shape of dataset : (6207, 40)

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F11965067%2F75861c40e86a4d2d10c044be79542436%2FCapture.JPG?generation=1704918894425981&alt=media" alt="">

Github Link : https://github.com/TusharPaul01/House-Price-Prediction

For more such dataset & code check : https://www.kaggle.com/tusharpaul2001

Code and data for "House Prices and Rents" House prices are from Case-Shiller and are not uploaded.

Abstract (en): How have house prices evolved over the long run? This paper presents annual house prices for 14 advanced economies since 1870. We show that real house prices stayed constant from the nineteenth to the mid-twentieth century, but rose strongly and with substantial cross-country variation in the second half of the twentieth century. Land prices, not replacement costs, are the key to understanding the trajectory of house prices. Rising land prices explain about 80 percent of the global house price boom that has taken place since World War II. Our findings have implications for the evolution of wealth-to-income ratios, the growth effects of agglomeration, and the price elasticity of housing supply.

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.

500,000+ Sample For Sale Homes Data in the US. Available fields include: "property_url","property_id","address","street_name","apartment","city","state","latitude","longitude","postcode","price","bedroom_number", "bathroom_number","price_per_unit","living_space","land_space","land_space_unit","broker_id","property_type","property_status","year_build",. "total_num_units","listing_age","RunDate","agency_name","agent_name","agent_phone","is_owned_by_zillow"

Can be modeled to analyze home sale price, geo-distribution of homes (every home has lat/lng).

Some properties are owned by zillow. Check out field is_owned_by_zillow, is_owned_by_zillow= 1 means this property is zillow owned. This dataset is for learning and educational purpose. If you are interested in building a similar dataset or for a larger scope and coverage, please contact info@barkingdata.com We specialize in web mining and web data harvesting from the world wide web (including mobile apps), we have built 5000+ datasets for researchers, analysts, scholars , retailers, ... Learn more from https://www.barkingdata.com

Check out Paradise Properties Vacation Rentals & Sales reviews by guests and hosts. Compare its performance to the Miramar Beach Airbnb market average. Decide if it’s the right choice for you.

Property Listings from the Maryland Department of Transportation's Office of Real Estate and Economic Development office. These properties are state-owned properties that are currently for sale, will be for sale, have a sale pending, or have recently sold.This map is updated when properties change categories or new properties become available. Use the interactive pop-up menus within the map for each property to view more information about the selected properties and to view the property in different maps and contexts. The state of Maryland is able to sell state-owned land periodically. This can involve public auctions as well. Please visit the Maryland Department of Transportation's Real Estate and Economic Development website for additional information: https://mdotrealestate.maryland.gov/Pages/default.aspx and check with their current tabular list of properties for the inventory.

Results of the lag order test of the quantity of residential supply on the residential price model.

Abstract (en): I develop and estimate a monetary business cycle model with nominal loans and collateral constraints tied to housing values. Demand shocks move housing and nominal prices in the same direction, and are amplified and propagated over time. The financial accelerator is not uniform: nominal debt dampens supply shocks, stabilizing the economy under interest rate control. Structural estimation supports two key model features: collateral effects dramatically improve the response of aggregate demand to housing price shocks; and nominal debt improves the sluggish response of output to inflation surprises. Finally, policy evaluation considers the role of house prices and debt indexation in affecting monetary policy trade-offs.