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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.

Replication data and Phyton programs for "Comparing automated valuation models for real estate assessment in the Santiago Metropolitan Region: A study on machine learning algorithms and hedonic pricing with spatial adjustments", PLOS ONE.

pleas cite our article: Do street-level scene perceptions affect housing prices in Chinese megacities? An analysis using open access datasets and deep learning

We examine how converting an industrial heritage site in Aarhus, Denmark, into a center for arts and culture affects nearby residents’ welfare. Using a hedonic house‑price model and a difference‑in‑differences design, we track apartment prices before and after the conversion. Prices within the neighborhood rose by 2.3–3 % relative to the rest of the city. This uplift represents a welfare gain of €17.5–21 million for the local community. As one of the few quasi‑experimental evaluations of creative‑led heritage revitalization, our study provides rigorous causal evidence of substantial indirect economic benefits. This evidence can guide future investments to convert disused industrial buildings into cultural and recreational spaces that serve local communities.

About Dataset

All the following text is copied directly from the original dataset used: https://www.kaggle.com/datasets/fedesoriano/the-boston-houseprice-data
The only difference is that features 12 and 13 have been removed for simplicity. See original link for a version with those features in place.

Similar Datasets

Gender Pay Gap Dataset: https://www.kaggle.com/fedesoriano/gender-pay-gap-dataset
California Housing Prices Data (5 new features!): https://www.kaggle.com/fedesoriano/california-housing-prices-data-extra-features
Company Bankruptcy Prediction: https://www.kaggle.com/fedesoriano/company-bankruptcy-prediction
Spanish Wine Quality Dataset: https://www.kaggle.com/datasets/fedesoriano/spanish-wine-quality-dataset

Context

The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978.

Attribute Information

Input features in order: 1) CRIM: per capita crime rate by town
2) ZN: proportion of residential land zoned for lots over 25,000 sq.ft.
3) INDUS: proportion of non-retail business acres per town
4) CHAS: Charles River dummy variable (1 if tract bounds river; 0 otherwise)
5) NOX: nitric oxides concentration (parts per 10 million) [parts/10M]
6) RM: average number of rooms per dwelling
7) AGE: proportion of owner-occupied units built prior to 1940
8) DIS: weighted distances to five Boston employment centres
9) RAD: index of accessibility to radial highways
10) TAX: full-value property-tax rate per $10,000 [$/10k]
11) PTRATIO: pupil-teacher ratio by town
[Original features 12 and 13 have been deliberately removed from this version of the dataset]

Output variable:
1) MEDV: Median value of owner-occupied homes in $1000's [k$]

Source

StatLib - Carnegie Mellon University

Relevant Papers

Harrison, David & Rubinfeld, Daniel. (1978). Hedonic housing prices and the demand for clean air. Journal of Environmental Economics and Management. 5. 81-102. 10.1016/0095-0696(78)90006-2. https://www.researchgate.net/profile/Daniel-Rubinfeld/publication/4974606_Hedonic_housing_prices_and_the_demand_for_clean_air/links/5c38ce85458515a4c71e3a64/Hedonic-housing-prices-and-the-demand-for-clean-air.pdf

Belsley, David A. & Kuh, Edwin. & Welsch, Roy E. (1980). Regression diagnostics: identifying influential data and sources of collinearity. New York: Wiley https://www.wiley.com/en-us/Regression+Diagnostics%3A+Identifying+Influential+Data+and+Sources+of+Collinearity-p-9780471691174

Abstract This paper estimates quantile hedonic price indexes for apartments in Belo Horizonte, Brazil, 1995-2012. From an urban economic point of view, the real estate is one example of a segmented market and for this reason we choose the quantile regression approach. The several results suggest that before 2004, when there was a lack of institutional mark for the real estate mortgages and macroeconomics environment was too uncertain, there a little appreciation in apartments prices. In this period there wasn’t a regular pattern in quantile appreciation. Since 2005, there was a great appreciation of apartments in all segments of the market, since the real estate mortgage increases due to the reformulation of the real estate mortgage institutional market. Before 2009, the appreciation was more pronounced in the highest segments. Since 2009, there was a reversion of the quantile appreciation pattern. The appreciation in lowest segments was higher than in the highest. Partly, this change can be attributed to countercyclical policies implemented by Brazilian Government which focus on families with medium and low incomes.

Rosen's (1974) theory of hedonic prices is implemented econometrically using recently developed nonparametric techniques to examine the influence of qualitative factors on the price of a house. Our ability to smooth categorical variables leads to greater generalization in the valuation process and provides a canvas for interactions between categorical and continuous variables that is difficult to exploit in parametric and semiparametric models. This is illustrated with a replication of a previously used partially linear model specification.

Similar Datasets

• Gender Pay Gap Dataset: LINK
• California Housing Prices Data (5 new features!): LINK
• Company Bankruptcy Prediction: LINK
• Spanish Wine Quality Dataset: LINK

Context

The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978.

Attribute Information

Input features in order: 1) CRIM: per capita crime rate by town 2) ZN: proportion of residential land zoned for lots over 25,000 sq.ft. 3) INDUS: proportion of non-retail business acres per town 4) CHAS: Charles River dummy variable (1 if tract bounds river; 0 otherwise) 5) NOX: nitric oxides concentration (parts per 10 million) [parts/10M] 6) RM: average number of rooms per dwelling 7) AGE: proportion of owner-occupied units built prior to 1940 8) DIS: weighted distances to five Boston employment centres 9) RAD: index of accessibility to radial highways 10) TAX: full-value property-tax rate per $10,000 [$/10k] 11) PTRATIO: pupil-teacher ratio by town 12) B: The result of the equation B=1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town 13) LSTAT: % lower status of the population

Output variable: 1) MEDV: Median value of owner-occupied homes in $1000's [k$]

Source

StatLib - Carnegie Mellon University

Relevant Papers

Harrison, David & Rubinfeld, Daniel. (1978). Hedonic housing prices and the demand for clean air. Journal of Environmental Economics and Management. 5. 81-102. 10.1016/0095-0696(78)90006-2. LINK

Belsley, David A. & Kuh, Edwin. & Welsch, Roy E. (1980). Regression diagnostics: identifying influential data and sources of collinearity. New York: Wiley LINK

The dataset contains structural attributes, locational information and prices for more than 139 thousand apartments in the city of Tehran (Iran). The data was collected from the largest national real estate website using a web crawler. It contains submission date, exact location, neighborhood name, base area, floor level, age of building, price per square meter, and total price for the entries of the past four years.

This paper examines the impact of earthquakes on residential property values using sales data from Oklahoma from 2006 to 2014. Before 2010, Oklahoma had only a couple of earthquakes per year that were strong enough to be felt by residents. Since 2010, seismic activity has increased, bring potentially damaging quakes several times each year and perceptible quakes every few days. Using hedonic models, we estimate that prices decline by 3 to 4 percent after a home has experienced a moderate earthquake measuring 4 or 5 on the Modified Mercalli Intensity Scale. Prices can decline up to 9.8 percent after a potentially damaging earthquake with intensity above 6. The correlations between measures of low intensity (MMI 3) quakes and prices are smaller and vary between specifications. Our findings are consistent with the experience of an earthquake revealing a new disamenity and risk which is then capitalized into house values.

Data used in the study was obtained from Property24 August 2021 to January 2022. Property24 is a property portal where property listings for sale and to rent from leading real estate agents are advertised. Property24 helps sellers, home buyers, and renters find apartments, houses, townhouses, vacant land, and farms across South Africa. Residential properties from two leading metros in the Eastern Cape Province, Buffalo City Municipality and Nelson Mandela Bay were considered. The data obtained was limited to the data displayed on each property by Property24 that evaluations obtained
The study adopted a parametric and non-parametric techniques to estimate the effects of hedonic housing characteristics on willingness to pay and willingness to accept. Hedonic regression model used multiple ordinary least squares regression to examine how each hedonic characteristic adds to the residential properties' entire worth. Ordinary least squares regression has been widely used in estimating house prices over the decades and it is well document in literature (McCord et al., 2018; My-Linh, 2020; Olamide & Adepoju, 2013; Owusu-Ansah, 2013). The study further adopted the Multilayer perceptron of the Artificial Neural Network which is a predictive model that is made up of multiple techniques that measure each self-contained component independently despite its linearity (Papadopoulos et al., 2021). The Artificial Neural Network has gained momentum recently due to its ability to ability to predict house prices (Ghorbani & Afgheh, 2017; Limsombunchai, 2004; Moreno et al., 2011). These techniques were used to estimate the effect of property attributes in influencing the willingness to accept and the willingness to pay in the Eastern Cape province of South Africa. Model specification and estimation techniques When building hedonic models, underlying assumptions of linearity, homoscedasticity, independence, normality, and correct model specification should be met. To meet these assumptions, the correct function form and the number of explanatory variables matters. The number of explanatory variables included in the model were determined after several pseudo model evaluations (Sirmans & Macpherson, 2003).

Hedonic price method (HPM) parameter estimate summary.

Boston Housing Dataset

The Boston Housing dataset is a well-known dataset in the field of predictive modeling and statistics. It contains information collected by the U.S. Census Service concerning housing in the area of Boston Mass.

Dataset Overview

• Number of Instances: 506
• Number of Attributes: 14 (including the target variable)

Attributes

The dataset includes the following features:

1. CRIM - Per capita crime rate by town.
2. ZN - Proportion of residential land zoned for lots over 25,000 sq. ft.
3. INDUS - Proportion of non-retail business acres per town.
4. CHAS - Charles River dummy variable (1 if tract bounds river; 0 otherwise).
5. NOX - Nitric oxides concentration (parts per 10 million).
6. RM - Average number of rooms per dwelling.
7. AGE - Proportion of owner-occupied units built prior to 1940.
8. DIS - Weighted distances to five Boston employment centers.
9. RAD - Index of accessibility to radial highways.
10. TAX - Full-value property tax rate per $10,000.
11. PTRATIO - Pupil-teacher ratio by town.
12. B - ( B ) stands for ( 1000(Bk - 0.63)^2 ) where ( Bk ) is the proportion of Black residents by town.
13. LSTAT - Percentage of lower status of the population.
14. MEDV - Median value of owner-occupied homes in $1000s (target variable).

Use Cases

This dataset can be used for:

• Regression Analysis: To predict the value of homes based on the features provided.
• Exploratory Data Analysis: To analyze the relationships between different variables.
• Machine Learning: As a benchmark dataset for testing regression models.

Citation

Details about the dataset and its original source can be found in the following reference:

• Harrison, D. and Rubinfeld, D. L. (1978). "Hedonic housing prices and the demand for clean air." J. Environ. Economics and Management, 5, 81-102.

Housing is among the most pressing issues in urban China and has received considerable scholarly attention. Researchers have primarily concentrated on identifying the factors that influence residential property prices and how such mechanisms function. However, few studies have examined the potential factors that influence housing prices from a big data perspective. In this article, we use a big data perspective to determine the willingness of buyers to pay for various factors. The opinions and geographical preferences of individuals for places can be represented by visit frequencies given different motivations. Check-in data from the social media platform Sina Visitor System is used in this article. Here, we use kernel density estimation (KDE) to analyse the spatial patterns of check-in spots (or places of interest, POIs) and employ the Getis-Ord method to identify the hot spots for different types of POIs in Shenzhen, China. New indexes are then proposed based on the hot-spot results as measured by check-in data to analyse the effects of these locations on housing prices. This modelling is performed using the hedonic price method (HPM) and the geographically weighted regression (GWR) method. The results show that the degree of clustering of POIs has a significant influence on housing values. Meanwhile, the GWR method has a better interpretive capacity than does the HPM because of the former method’s ability to capture spatial heterogeneity. This article integrates big social media data to expand the scope (new study content) and depth (study scale) of housing price research to an unprecedented degree.

In this empirical analysis, we estimate the impact of vacancy, neglect associated with property-tax delinquency, and foreclosures on the value of neighboring homes using parcel-level observations. Numerous studies have estimated the impact of foreclosures on neighboring properties, and these papers theorize that the foreclosure impact works partially through creating vacant and neglected homes. To our knowledge, this is only the second attempt to estimate the impact of vacancy itself and the first to estimate the impact of tax-delinquent properties on neighboring home sales. We link vacancy observations from Postal Service data with property-tax delinquency and sales data from Cuyahoga County (the county encompassing Cleveland, Ohio). We estimate hedonic price models with corrections for spatial autocorrelation. We find that an additional property within 500 feet that is vacant, delinquent, or both reduces the home’s selling price by at least 1.3 percent. In low-poverty areas, tax-current foreclosed homes have large negative impacts of 4.6 percent. In high-poverty areas, we observe positive correlations of sale prices with tax-current foreclosures and negative correlations with tax-delinquent foreclosures. This may reflect selective foreclosing on better maintained properties or better maintenance by tax-paying foreclosure auction winners. The marginal medium-poverty census tracts display the largest negative responses to vacancy and delinquency in nearby nonforeclosed homes.

Average nearest neighbour summary.

In 2009, Cuyahoga County, Ohio, which contains Cleveland and 58 other municipalities, created the Cuyahoga County Land Reutilization Corporation. This land bank was established to acquire low-value properties, mitigate blighted housing, help stabilize neighborhoods, and slow the decline of property values. As of September 2013, the land bank had acquired 3,405 properties and demolished 1,853 structures. This empirical study evaluates the effectiveness of the land bank by estimating spatially corrected hedonic price models using sales near the land bank homes. In the six months before they are purchased by the land bank, the distressed properties are estimated to lower the sale price of nearby homes (within 500 feet) by 5.2 percent. The negative externality from the distressed properties decreases to 4.4 percent once the land bank takes possession.

Descriptive statistics of the municipal hedonic indices.

POI types and aggregated information.

Measurement methods and housing characteristic variable signs.