https://raw.githubusercontent.com/Masterx-AI/Project_Housing_Price_Prediction_/main/hs.jpg" alt="">

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.

Context

This dataset was actually made to check the correlations between a housing price index and its crime rate. Rise and fall of housing prices can be due to various factors with obvious reasons being the facilities of the house and its neighborhood. Think of a place like Detroit where there are hoodlums and you don't want to end up buying a house in the wrong place. This data set will serve as historical data for crime rate data and this in turn can be used to predict whether the housing price will rise or fall. Rise in housing price will suggest decrease in crime rate over the years and vice versa.

Content

The headers are self explanatory. index_nsa is the housing price non seasonal index.

Acknowledgements

Thank you to my team who helped in achieving this.

Inspiration

https://www.kaggle.com/marshallproject/crime-rates https://catalog.data.gov/dataset/fhfa-house-price-indexes-hpis Data was collected from these 2 sources and merged to get the resulting dataset.

Single Family Home Prices in the United States increased to 415200 USD in October from 412300 USD in September of 2025. This dataset provides - United States Existing Single Family Home Prices- actual values, historical data, forecast, chart, statistics, economic calendar and news.

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

Housing Index in China remained unchanged at -2.20 percent in October. This dataset provides the latest reported value for - China Newly Built House Prices YoY Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Nahb Housing Market Index in the United States increased to 38 points in November from 37 points in October of 2025. This dataset provides the latest reported value for - United States Nahb Housing Market Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Key information about House Prices Growth

• US house prices grew 3.3% YoY in Sep 2025, following an increase of 4.1% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 1992 to Sep 2025, with an average growth rate of -12.4%.
• House price data reached an all-time high of 17.7% in Sep 2021 and a record low of -12.4% in Dec 2008.

This data sets out the percentage of residents of the Cambridge housing sub-region who are unable to afford housing, based on contemporary income data and housing costs, broken down into percentage for 1, 2 and 3 bedroom homes. The data comes from the housing sub-region's Strategic Housing Market Assessment, or SHMA, which is updated regularly. The data provided in this open data set comes from: SHMA 2013, based on 2011/12 data SHMA 2012, based on 2009/10 data SHMA 2010, based on 2008/9 data SHMA 2009, based on mostly 2007/8 data The data is all published in chapters of our strategic housing market assessment which are used as part of our calculations around the need for affordable housing, particularly where we need to work out the proportion of people unlikely to be able to afford housing via the private market (owned or rented) and thus potentially in need of "sub market" or affordable housing.

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.

Context

California House Pricing(1990)

Content

Prediction of House values in California in the year 1990.

Acknowledgements

Aurelien Geron

Inspiration

Will we see the fall in housing prices in the near future?

Rental Affordability Analysis Based on Median Income

Trends in Tier-Based Affordability Across the U.S

By Zillow Data [source]

About this dataset

This dataset contains rental affordability data for different regions in the US, giving valuable insights into regional rental markets. Renters can use this information to identify where their budget will go the farthest. The cities are organized by rent tier in order to analyze affordability trends within and between different housing stock types. Within each region, the data includes median household income, Zillow Rent Index (ZRI), and percent of income spent on rent.

The Zillow Home Value Forecast (ZHVF) is used to calculate future combined mortgage pay/rent payments in each region using current median home prices, actual outstanding debt amounts and 30-year fixed mortgage interest rates reported through partnership with TransUnion credit bureau. Zillow also provides a breakdown of cash vs financing purchases for buyers looking for an investment or cash option solution.

This dataset provides an effective tool for consumers who want to better understand how their budget fits into diverse rental markets across the US; from condominiums and co-ops, multifamily residences with five or more units, duplexes and triplexes - every renter can determine how their housing budget should be adjusted as they consider multiple living possibilities throughout the country based on real-time price data!

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How to use the dataset

Introduction

Getting Started

• First, you'll need to download the TieredAffordability_Rental.csv dataset from this Kaggle page onto your computer or device.

• After downloading the data set onto your device, open it with any CSV viewing software of your choice (ex: Excel). It will include columns for RegionName**RegionName** , homes type/housing stock (All Homes or Condo/Co-op) SizeRank , Rent tier tier , Date date , median household income income , Zillow Rent Index zri and PercentIncomeSpentOnRent percentage (what portion of monthly median house-hold goes toward monthly mortgage payment) .

• To begin analyzing rental prices across different regions using this dataset, look first at column four: SizeRank; which ranks each region based on size - smallest regions listed first and largest at last - so that you can compare a similar range of Regions when looking at affordability by home sizes larger than one unit multiplex dwellings.*Duples/Triplex*. Once there is an understanding of how all homes compare overall now it is time to consider home types Multifamily 5+ units according to rent tiers tier .

• Next, choose one or more region(s) for comparison based on their rank in SizeRank column –so that all information gathered about them reflects what portionof households fall into certain categories ; eg; All Homes / Small Home /Large Home / MultiPlex Dwelling and what tier does each size rank falls into eg.: Affordable/Slightly Expensive/ Moderately Expensive etc.. This will enable further abstraction from other elements like date vs inflation rate per month or periodical intervals set herein by Rate segmentation i e dates givenin ‘Date’Columns – making the task easier and more direct while analyzing renatalAffordibility Analysis Based On Median Income zri 00 zwi & PCISOR 00 PCIRO

Research Ideas

• Use the PercentIncomeSpentOnRent column to compare rental affordability between regions within a particular tier and determine optimal rent tiers for relocating families.
• Analyze how market conditions are affecting rental affordability over time by using the income, zri, and PercentageIncomeSpentOnRent columns.
• Identify trends in housing prices for different tiers over the years by comparing SizeRank data with Zillow Home Value Forecast (ZHVF) numbers across different regions in order to identify locations that may be headed up or down in terms of home values (and therefore rent levels)

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: TieredAffordability_Rental.csv | Column name | Description | |:-----------------------------|:-------------------------------------------------------------| | RegionName | The name of the region. (String) ...

Housing Index in the United States decreased to 435.40 points in September from 435.60 points in August of 2025. This dataset provides the latest reported value for - United States House Price Index MoM Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

About this Dataset

Class materials for REE 6315 in Fall 2017. We will be using this data as an ongoing example throughout the course. Students will need this data to complete in class quizzes and out of class assignments. Please also download the free real estate listing data also required for the course: https://www.dataandsons.com/categories/sales_&_transactions/u.s._real_estate_inventory

Data was sourced by combining open data sources with instructors original content.

Category

Classroom Datasets

Keywords

housing,equity,realestate,transactions,sales

Row Count

929

Price

$75.00

The establishment of Xiong’an New Area is an important decision for China to remove non-capital functions. The paper takes the “Xiong’an New Area” policy as a quasi-natural experiment and uses the Synthetic control method and the Difference-in-Difference method to study the influence of establishing Xiongan New Area on the quantity and price of new and second-hand housing markets in Beijing. The study finds that after the establishment of Xiongan New Area, the overall quantity of new houses in Beijing fall, while that of second-hand houses rise. The new housing price rises steadily, the second-hand housing price has an obvious downward trend. On whole, the "Xiong’an New Area" policy has a great influence on the second-hand housing quantity, the new housing price and the second-hand housing price index in Beijing. Based on the empirical results, in order to promote the rational development of Beijing's real estate market through the "Xiong’an New Area" policy, and to achieve the national policy goal of "no speculation on housing" and "housing for housing", we need to strengthen the planning and construction of the area.

Have been tracking the thailand housing market for a few years, in the last couple of years, for sale properties have been steadily increasing between 2020 and late 2021; coming into 2022, the housiing market seems went down in temrs of property listings, https://barkingdata.com/static/upload/image/20220423/1650710255930743.png" alt="listings trend">

The attached dataset is generated via AI based public daa mining technology. Researchers can use this dataset to do various analysis because the number of fields of this dataset includes a lot of housing attributes such as property type, pricing, zipcode, living space size, bedrooms, bathrooms, city, state, lat/lng, home created date, land space, agent name, agent info, funished, premium type etc.. 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

Negative Equity Trends in US Housing Markets

Time Series Data Across Regions and Housing Types

By Zillow Data [source]

About this dataset

• This unique dataset explores the trends in negative equity within US housing markets from 2011 to 2017, allowing users to uncover the various factors and determinants that affected the outcome in each market. With data provided on all home types such as single-family homes, condominiums, and co-ops, as well as special metrics such as cash buyers and affordability analyses, you will be able to gain a comprehensive understanding of how these forces have interacted over time. Using this data you can not only learn more about historical behavior but also make predictions for future trends in these impacts.

• In addition to data collected by Zillow through their own internal resources, they have also partnered with TransUnion and other affiliate sources to give an even more precise look into what has been driving these changing dynamics across US housing markets. Such information includes negative equity metrics which allow us to track actual outstanding home-related debt amounts over time - a valuable resource when evaluating potential investments or relocations!

• And of course with any dataset there are a few guiding principles that one should take note of before delving in – this is especially true when it comes down to copyright issues or prohibited uses; though all data can be freely obtained here for public use - clear attribution of such information is legally required at all times (as stated on Zillow’s very own Terms & Conditions page). Furthermore additional resources such as Mortgage Rate Series or Jumbo Mortgages are also available through Zillow; again making sure that appropriate disclaimers are read before utilizing them.

Regardless this little treasure trove of knowledge is waiting at your fingertips – whether you’re trying your luck investing wise or just looking for an area where renting rates are equitable compared real estate values; it provides everything you need understand regional housing market fluctuations over the last half decade!

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Featured Notebooks

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How to use the dataset

This dataset provides historical and current trends in negative equity (the amount a mortgage is underwater) across the United States. It contains negative equity data from Zillow, one of the leading real estate data providers. The dataset covers all housing types (including single family, condominiums and co-ops). Additionally, it includes cash buyers share, mortgage affordability index, rental affordability index and other relative measures of affordability for US metro areas. This guide will help you understand how to use this data set for your own analysis.

Overview of Covered Data:

The dataset contains time series data that shows your current trend in negative equity rate as well as some associated metrics across different scales such as region, county, city and MSA level. To access this information you will need to take following columns into consideration while using this data set:

• RegionName: Name of the region (e.g., city/county/MSA)
• SizeRank: Ranking of the region by size
• RegionType: Type of region (e.g., city/county/state)
• StateName: Name of the state
• MSA: Metropolitan Statistical Area FORMAT_4C A4 RINFOX_ RTI Information Exchange File Format [multi value 9] FORMAT_3E A3 FITS Flexible Image Transport System VERSION 4C 3E 1 Language Indicator 0 0 1 1 DONTCOPY 536880031 FILEEXTN 3 Stream Type buffer 'USTD' file version 2 HNEED 8 FILETYPE 'UDIO' creation date 05 FEB 1985 Source FMT0025 APPLICAT TRAINFORM File Organization Spooled Files DF140520 Header Block Length in Words 682 with Header Offset 636 / ULQUACK INTLCHAN * ETBFMT(V7R2),D*RECORD ACCOUNT CRFTIME FT240187 batch process status continuous Availability Continuous Version number V03C02 LOADAT AT04

Research Ideas

• Analyzing which markets have been disproportionately affected by the housing crisis and utilizing this information to inform investment strategies and...

Housing Starts in the United States decreased to 1307 Thousand units in August from 1429 Thousand units in July of 2025. This dataset provides the latest reported value for - United States Housing Starts - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

• Objective: To predict the prices of houses in the City of Melbourne
• Approach: Using Decision Tree and Random Forest https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2Ffc6fb7d0bd8e854daf7a6f033937a397%2FPicture1.png?generation=1693489996707941&alt=media" alt="">
• Data Cleaning:
• Date column is shown as a character vector which is converted into a date vector using the library ‘lubridate’
• We create a new column called age to understand the age of the house as it can be a factor in the pricing of the house. We extract the year from column ‘Date’ and subtract it from the column ‘Year Built’
• We remove 11566 records which have missing values
• We drop columns which are not significant such as ‘X’, ‘suburb’, ‘address’, (we have kept zipcode as it serves the purpose in place of suburb and address), ‘type’, ‘method’, ‘SellerG’, ‘date’, ‘Car’, ‘year built’, ‘Council Area’, ‘Region Name’
• We split the data into ‘train’ and ‘test’ in 80/20 ratio using the sample function
• Run libraries ‘rpart’, ‘rpart.plot’, ‘rattle’, ‘RcolorBrewer’
• Run decision tree using the rpart function. ‘Price’ is the dependent variable https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2F6065322d19b1376c4a341a4f22933a51%2FPicture2.png?generation=1693490067579017&alt=media" alt="">
• Average price for 5464 houses is $1084349
• Where building area is less than 200.5, the average price for 4582 houses is $931445. Where building area is less than 200.5 & age of the building is less than 67.5 years, the avg price for 3385 houses is $799299.6.
• $4801538 is the Highest average prices of 13 houses where distance is lower than 5.35 & building are is >280.5
  https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2F136542b7afb6f03c1890bae9b07dc464%2FDecision%20Tree%20Plot.jpeg?generation=1693490124083168&alt=media" alt="">
• We use the caret package for tuning the parameter and the optimal complexity parameter found is 0.01 with RMSE 445197.9 https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2Feb1633df9dd61ba3a51574873b055fd0%2FPicture3.png?generation=1693490163033658&alt=media" alt="">
• We use library (Metrics) to find out the RMSE ($392107), MAPE (0.297) which means an accuracy of 99.70% and MAE ($272015.4)
• Variables ‘postcode’, longitude and building are the most important variables
• Test$Price indicates the actual price and test$predicted indicates the predicted price for particular 6 houses. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2F620b1aad968c9aee169d0e7371bf3818%2FPicture4.png?generation=1693490211728176&alt=media" alt="">
• We use the default parameters of random forest on the train data https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2Fe9a3c3f8776ee055e4a1bb92d782e19c%2FPicture5.png?generation=1693490244695668&alt=media" alt="">
• The below image indicates that ‘Building Area’, ‘Age of the house’ and ‘Distance’ are the most important variables that affect the price of the house. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2Fc14d6266184db8f30290c528d72b9f6b%2FRandom%20Forest%20Variables%20Importance.jpeg?generation=1693490284920037&alt=media" alt="">
• Based on the default parameters, RMSE is $250426.2, MAPE is 0.147 (accuracy is 99.853%) and MAE is $151657.7
• Error starts to remain constant between 100 to 200 trees and thereafter there is almost minimal reduction. We can choose N tree=200. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2F365f9e8587d3a65805330889d22f9e60%2FNtree%20Plot.jpeg?generation=1693490308734539&alt=media" alt="">
• We tune the model and find mtry = 3 has the lowest out of bag error
• We use the caret package and use 5 fold cross validation technique
• RMSE is $252216.10 , MAPE is 0.146 (accuracy is 99.854%) , MAE is $151669.4
• We can conclude that Random Forest give us more accurate results as compared to Decision Tree
• In Random Forest , the default parameters (N tree = 500) give us lower RMSE and MAPE as compared to N tree = 200. So we can proceed with those parameters.

The average resale house price in Canada was forecast to reach nearly ******* Canadian dollars in 2026, according to a January forecast. In 2024, house prices increased after falling for the first time since 2019. One of the reasons for the price correction was the notable drop in transaction activity. Housing transactions picked up in 2024 and are expected to continue to grow until 2026. British Columbia, which is the most expensive province for housing, is projected to see the average house price reach *** million Canadian dollars in 2026. Affordability in Vancouver Vancouver is the most populous city in British Columbia and is also infamously expensive for housing. In 2023, the city topped the ranking for least affordable housing market in Canada, with the average homeownership cost outweighing the average household income. There are a multitude of reasons for this, but most residents believe that foreigners investing in the market cause the high housing prices. Victoria housing market The capital of British Columbia is Victoria, where housing prices are also very high. The price of a single family home in Victoria's most expensive suburb, Oak Bay was *** million Canadian dollars in 2024.

Existing Home Sales in the United States increased to 4100 Thousand in October from 4050 Thousand in September of 2025. This dataset provides the latest reported value for - United States Existing Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.