What is the House Price Dataset dataset?

545 Indian residential property listings with 13 features including price, area, bedrooms, and amenities. Clean dataset with no missing values—ideal for house price prediction, regression modeling, and real estate ML projects.

How can I download the House Price Dataset dataset?

You can download the House Price Dataset dataset in CSV or Parquet format directly from this page. Each table has its own download buttons.

What format is the House Price Dataset dataset available in?

This dataset is available in CSV and Parquet formats. CSV is great for spreadsheet applications, while Parquet is optimized for data analysis tools like Pandas and DuckDB.

What is the license for the House Price Dataset dataset?

This dataset is available under the CC0: Public Domain license. See the full license at https://creativecommons.org/publicdomain/zero/1.0/

How many tables are in the House Price Dataset dataset?

The House Price Dataset dataset contains 1 table: house_price.

House Price Dataset

Last updated 1 weeks ago•January 2, 2026

Source:Kaggle

License:CC0: Public Domain

Version:1.0

Time:Cross-sectional snapshot

Location:India

Created by Dataset Agent

Overview

The House Price Dataset is a curated collection of 545 residential property listings from the Indian real estate market, designed specifically for machine learning practitioners and data science students. Unlike many housing datasets that require extensive cleaning, this dataset arrives analysis-ready with zero missing values across all features—making it an ideal starting point for regression modeling and price prediction projects.

This dataset contains 545 property listings with 13 distinct features and 0% missing values across all columns.

View Source

SQL

SELECT COUNT(*) AS row_count FROM house_price.csv

Data

Row Count
545
1 row

Each record represents a unique residential property with comprehensive information spanning physical characteristics (area in square feet, bedrooms, bathrooms, stories), location attributes (main road access, preferred area designation), and amenities (air conditioning, basement, hot water heating, guest room, parking spaces, and furnishing status).

How This Dataset Compares

When selecting a housing dataset for machine learning projects, practitioners often compare multiple options. Here's how this dataset positions against other popular choices:

This dataset's strength lies in its clean structure and balanced feature mix—13 features provide enough complexity for meaningful analysis without overwhelming beginners, while the zero missing values eliminate preprocessing hurdles.

Key Statistics and Market Insights

Property prices range from ₹1,750,000 to ₹13,300,000 with a median of ₹4,340,000 and mean of ₹4.77 million—a 7.6x spread indicating significant market segmentation.

View Source

SQL

SELECT MIN(price) AS min_price, MAX(price) AS max_price, AVG(price) AS avg_price, MEDIAN (price) AS median_price FROM house_price.csv

Data

Min Price	Max Price	Avg Price	Median Price
1,750,000	13,300,000	4,766,729.25	4,340,000
1 row

Property areas span 1,650 to 16,200 square feet with an average of 5,151 sq ft—nearly 10x variation suitable for exploring non-linear price relationships.

View Source

SQL

SELECT MIN(area) AS min_area, MAX(area) AS max_area, AVG(area) AS avg_area FROM house_price.csv

Data

Min Area	Max Area	Avg Area
1,650	16,200	5,150.54
1 row

Price Drivers and Feature Analysis

View Source

SQL

SELECT bedrooms, COUNT(*) AS count, ROUND(AVG(price)) AS avg_price FROM house_price.csv GROUP BY bedrooms ORDER BY bedrooms

Data

Bedrooms	Count	Avg Price (₹)
1 BR	2	2,712,500
2 BR	136	3,632,022
3 BR	300	4,954,598
4 BR	95	5,729,758
5 BR	10	5,819,800
6 BR	2	4,791,500
6 rows

3-bedroom homes dominate with 300 listings (55% of dataset), followed by 2-bedroom (136, 25%) and 4-bedroom (95, 17%) properties.

View Source

SQL

SELECT bedrooms, COUNT(*) AS count, ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 1) AS percentage FROM house_price.csv GROUP BY bedrooms ORDER BY count DESC

Data

Bedrooms	Count	Percentage
3	300	55
2	136	25
4	95	17.4
3 rows

Amenity Premium Analysis

Understanding which features command price premiums is essential for feature importance analysis and real-world valuation models:

View Source

SQL

Multiple queries comparing average prices WITH / WITHOUT EACH amenity

Data

Feature	With Feature (₹)	Without Feature (₹)	Premium %
Air Conditioning	6,013,221	4,191,940	+43%
Preferred Area	5,879,046	4,425,299	+33%
Main Road Access	4,991,777	3,398,905	+47%
Basement	5,242,615	4,509,966	+16%
4 rows

Key Finding: Air conditioning delivers the highest price premium at 43% (₹6.01M vs ₹4.19M). Main road access shows the strongest absolute impact at 47% premium—critical insights for feature engineering in predictive models.

Variable Correlations

The area-price correlation is 0.54, indicating a moderate positive relationship—larger homes command higher prices, but other factors significantly influence valuation.

View Source

SQL

SELECT ROUND(CORR(area, price), 3) AS area_price_correlation FROM house_price.csv

Data

Area Price Correlation
0.54
1 row

This moderate correlation (rather than strong) makes the dataset valuable for demonstrating multicollinearity concepts and the importance of categorical features in regression models. The boolean amenity features often explain variance that area alone cannot capture.

Property Distribution Patterns

View Source

SQL

SELECT furnishingstatus, COUNT(*) AS count FROM house_price.csv GROUP BY furnishingstatus

Data

Status	Count
Semi-furnished	227
Unfurnished	178
Furnished	140
3 rows

Furnished homes average ₹5.5 million versus ₹4.0 million for unfurnished—a 38% premium that demonstrates clear categorical feature impact on price.

View Source

SQL

SELECT furnishingstatus, ROUND(AVG(price)) AS avg_price FROM house_price.csv GROUP BY furnishingstatus

Data

Furnishingstatus	Avg Price
furnished	5,500,000
unfurnished	4,000,000
2 rows

View Source

SQL

SELECT stories, COUNT(*) AS count, ROUND(AVG(price)) AS avg_price FROM house_price.csv GROUP BY stories ORDER BY stories

Data

Stories	Count	Avg Price (₹)
1 Story	227	4,170,659
2 Stories	238	4,764,074
3 Stories	39	5,685,436
4 Stories	41	7,208,450
4 rows

Four-story properties command ₹7.2 million average—72% higher than single-story homes at ₹4.17 million. This non-linear relationship with stories makes the dataset excellent for polynomial feature exploration.

View Source

SQL

SELECT stories, ROUND(AVG(price)) AS avg_price FROM house_price.csv WHERE stories IN (1, 4) GROUP BY stories

Data

Stories	Avg Price
1	4,170,659
4	7,208,450
2 rows

Sample Records

Top 5 Most Expensive Properties

#	Price (₹)	Area (Sq Ft)	Bedrooms	Bathrooms	Stories	AC	Furnishing
1	13,300,000	7,420	4	2	3	Yes	Furnished
2	12,250,000	8,960	4	4	4	Yes	Furnished
3	12,250,000	9,960	3	2	2	No	Semi-furnished
4	12,215,000	7,500	4	2	2	Yes	Furnished
5	11,410,000	7,420	4	1	2	Yes	Furnished
5 rows

View Source

SQL

SELECT price, area, bedrooms, bathrooms, stories, airconditioning, furnishingstatus FROM house_price.csv ORDER BY price DESC LIMIT 5

Data

Price (₹)	Area (Sq Ft)	Bedrooms	Bathrooms	Stories	AC	Furnishing
13,300,000	7,420	4	2	3	Yes	Furnished
12,250,000	8,960	4	4	4	Yes	Furnished
12,250,000	9,960	3	2	2	No	Semi-furnished
12,215,000	7,500	4	2	2	Yes	Furnished
11,410,000	7,420	4	1	2	Yes	Furnished
5 rows

Note that the highest-priced property (₹13.3M) is not the largest by area—it's the 7,420 sq ft furnished home with AC. This illustrates why multi-feature models outperform single-variable approaches.

Data Quality and Limitations

This dataset represents a cross-sectional snapshot of the Indian real estate market. Prices reflect historical data and should be used for educational and analytical purposes only—not for actual investment decisions.

When using this dataset, consider these characteristics:

Zero Missing Values: All 545 records are complete across all 13 columns—no imputation required
Sample Size: 545 records support traditional ML algorithms well; may be insufficient for deep learning without augmentation
Geographic Scope: Properties appear concentrated in Indian metros; findings may not generalize to other markets
No Temporal Data: Timestamps are absent, preventing time-series analysis or trend modeling
Feature Gaps: Common variables like property age, lot size, neighborhood details, and school district ratings are not included

Recommended Model Approaches

Based on the dataset's structure and feature mix, these algorithms typically perform well:

Feature engineering opportunities include: creating price-per-square-foot ratios, binning continuous variables like area into categories, generating interaction terms between amenities, and one-hot encoding the furnishing status variable.

Table Overview

house_price

Contains 545 rows and 13 columns. Column types: 6 numeric, 1 text, 6 boolean.

545 rows13 columns

house_price

545

rows

columns

Data Preview

Scroll to see more

price	area	bedrooms	bathrooms	stories	mainroad	guestroom	basement	hotwaterheating	airconditioning	parking	prefarea	furnishingstatus
13,300,000	7,420	4	2	3	true	false	false	false	true	2	true	furnished
12,250,000	8,960	4	4	4	true	false	false	false	true	3	false	furnished
12,250,000	9,960	3	2	2	true	false	true	false	false	2	true	semi-furnished
12,215,000	7,500	4	2	2	true	false	true	false	true	3	true	furnished
11,410,000	7,420	4	1	2	true	true	true	false	true	2	false	furnished

Row 1

price13,300,000

area7,420

bedrooms4

bathrooms2

stories3

+8 more columns

Row 2

price12,250,000

area8,960

bedrooms4

bathrooms4

stories4

+8 more columns

Row 3

price12,250,000

area9,960

bedrooms3

bathrooms2

stories2

+8 more columns

Showing 5 of 545 rows

Data Profile

545

rows

columns

100%

complete

345.9 KB

estimated size

Column Types

6 Numeric1 Text6 Boolean

High-Cardinality Columns

Columns with many unique values (suitable for identifiers or categorical features)

area(284 unique values)

Data Dictionary

house_price

Column	Type	Description	Example
`price`	numeric	Monetary value	13300000, 12250000
`area`	numeric	Numeric value (range: 7,420 - 9,960)	7420, 8960
`bedrooms`	numeric	Numeric value (range: 3 - 4)	4, 4
`bathrooms`	numeric	Numeric value (range: 1 - 4)	2, 4
`stories`	numeric	Numeric value (range: 2 - 4)	3, 4
`mainroad`	boolean	Boolean flag	true, true
`guestroom`	boolean	Boolean flag	false, false
`basement`	boolean	Boolean flag	false, false
`hotwaterheating`	boolean	Boolean flag	false, false
`airconditioning`	boolean	Boolean flag	true, true
`parking`	numeric	Numeric value (range: 2 - 3)	2, 3
`prefarea`	boolean	Boolean flag	true, false
`furnishingstatus`	string	Status indicator	"furnished", "furnished"