The exercise after this contains questions that are based on the housing dataset.

1. How many houses have a waterfront? a. 21000 b. 21450 c. 163 d. 173

2. How many houses have 2 floors? a. 2692 b. 8241 c. 10680 d. 161

3. How many houses built before 1960 have a waterfront? a. 80 b. 7309 c. 90 d. 92

4. What is the price of the most expensive house having more than 4 bathrooms? a. 7700000 b. 187000 c. 290000 d. 399000

5. For instance, if the ‘price’ column consists of outliers, how can you make the data clean and remove the redundancies? a. Calculate the IQR range and drop the values outside the range. b. Calculate the p-value and remove the values less than 0.05. c. Calculate the correlation coefficient of the price column and remove the values less than the correlation coefficient. d. Calculate the Z-score of the price column and remove the values less than the z-score.

6. What are the various parameters that can be used to determine the dependent variables in the housing data to determine the price of the house? a. Correlation coefficients b. Z-score c. IQR Range d. Range of the Features

7. If we get the r2 score as 0.38, what inferences can we make about the model and its efficiency? a. The model is 38% accurate, and shows poor efficiency. b. The model is showing 0.38% discrepancies in the outcomes. c. Low difference between observed and fitted values. d. High difference between observed and fitted values.

8. If the metrics show that the p-value for the grade column is 0.092, what all inferences can we make about the grade column? a. Significant in presence of other variables. b. Highly significant in presence of other variables c. insignificance in presence of other variables d. None of the above

9. If the Variance Inflation Factor value for a feature is considerably higher than the other features, what can we say about that column/feature? a. High multicollinearity b. Low multicollinearity c. Both A and B d. None of the above

House Price Prediction Dataset.

The dataset contains 2000 rows of house-related data, representing various features that could influence house prices. Below, we discuss key aspects of the dataset, which include its structure, the choice of features, and potential use cases for analysis.

1. Dataset Features

The dataset is designed to capture essential attributes for predicting house prices, including:

Area: Square footage of the house, which is generally one of the most important predictors of price. Bedrooms & Bathrooms: The number of rooms in a house significantly affects its value. Homes with more rooms tend to be priced higher. Floors: The number of floors in a house could indicate a larger, more luxurious home, potentially raising its price. Year Built: The age of the house can affect its condition and value. Newly built houses are generally more expensive than older ones. Location: Houses in desirable locations such as downtown or urban areas tend to be priced higher than those in suburban or rural areas. Condition: The current condition of the house is critical, as well-maintained houses (in 'Excellent' or 'Good' condition) will attract higher prices compared to houses in 'Fair' or 'Poor' condition. Garage: Availability of a garage can increase the price due to added convenience and space. Price: The target variable, representing the sale price of the house, used to train machine learning models to predict house prices based on the other features.

2. Feature Distributions

Area Distribution: The area of the houses in the dataset ranges from 500 to 5000 square feet, which allows analysis across different types of homes, from smaller apartments to larger luxury houses. Bedrooms and Bathrooms: The number of bedrooms varies from 1 to 5, and bathrooms from 1 to 4. This variance enables analysis of homes with different sizes and layouts. Floors: Houses in the dataset have between 1 and 3 floors. This feature could be useful for identifying the influence of multi-level homes on house prices. Year Built: The dataset contains houses built from 1900 to 2023, giving a wide range of house ages to analyze the effects of new vs. older construction. Location: There is a mix of urban, suburban, downtown, and rural locations. Urban and downtown homes may command higher prices due to proximity to amenities. Condition: Houses are labeled as 'Excellent', 'Good', 'Fair', or 'Poor'. This feature helps model the price differences based on the current state of the house. Price Distribution: Prices range between $50,000 and $1,000,000, offering a broad spectrum of property values. This range makes the dataset appropriate for predicting a wide variety of housing prices, from affordable homes to luxury properties.

3. Correlation Between Features

A key area of interest is the relationship between various features and house price: Area and Price: Typically, a strong positive correlation is expected between the size of the house (Area) and its price. Larger homes are likely to be more expensive. Location and Price: Location is another major factor. Houses in urban or downtown areas may show a higher price on average compared to suburban and rural locations. Condition and Price: The condition of the house should show a positive correlation with price. Houses in better condition should be priced higher, as they require less maintenance and repair. Year Built and Price: Newer houses might command a higher price due to better construction standards, modern amenities, and less wear-and-tear, but some older homes in good condition may retain historical value. Garage and Price: A house with a garage may be more expensive than one without, as it provides extra storage or parking space.

4. Potential Use Cases

The dataset is well-suited for various machine learning and data analysis applications, including:

House Price Prediction: Using regression techniques, this dataset can be used to build a model to predict house prices based on the available features. Feature Importance Analysis: By using techniques such as feature importance ranking, data scientists can determine which features (e.g., location, area, or condition) have the greatest impact on house prices. Clustering: Clustering techniques like k-means could help identify patterns in the data, such as grouping houses into segments based on their characteristics (e.g., luxury homes, affordable homes). Market Segmentation: The dataset can be used to perform segmentation by location, price range, or house type to analyze trends in specific sub-markets, like luxury vs. affordable housing. Time-Based Analysis: By studying how house prices vary with the year built or the age of the house, analysts can derive insights into the trends of older vs. newer homes.

5. Limitations and ...

Gain a complete view of the real estate market with our Zillow datasets. Track price trends, rental/sale status, and price per square foot with the Zillow Price History dataset and explore detailed listings with prices, locations, and features using the Zillow Properties Listing dataset. Over 134M records available Price starts at $250/100K records Data formats are available in JSON, NDJSON, CSV, XLSX and Parquet. 100% ethical and compliant data collection Included datapoints:

Zpid
City
State
Home Status
Street Address
Zipcode
Home Type
Living Area Value
Bedrooms
Bathrooms
Price
Property Type
Date Sold
Annual Homeowners Insurance
Price Per Square Foot
Rent Zestimate
Tax Assessed Value
Zestimate
Home Values
Lot Area
Lot Area Unit
Living Area
Living Area Units
Property Tax Rate
Page View Count
Favorite Count
Time On Zillow
Time Zone
Abbreviated Address
Brokerage Name
And much more

Average house prices are derived from data supplied by the mortgage lending agencies on loans approved by them rather than loans paid. In comparing house prices figures from one period to another, account should be taken of the fact that changes in the mix of houses (incl apartments) will affect the average figures. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. Excluding apartments, measured in € Figure changed on the 27/6/16 as revised data received from the Local authority .hidden { display: none }

This table shows the average purchase price that has been paid in the reporting period for existing own homes purchased by a private individual. The average purchase price of existing own homes may differ from the price index of existing own homes. The average purchase price is no indicator for price developments of owner-occupied residential property. The average purchase price reflects the average price of dwellings sold in a particular period. The fact that de dwellings sold differs from one period to another is not taken into account. The following instance explains which problems are entailed by the continually changing of the quality of the dwellings sold. Suppose in February of a particular year mainly big houses with extensive gardens beautifully situated alongside canals are sold, whereas in March many small terraced houses are sold. In that case the average purchase price in February will be higher than in March but this does not mean that house prices are increased. See note 3 for a link to the article 'Why the average purchase price is not an indicator'.

Data available from: 1995

Status of the figures: The figures in this table are immediately definitive. The calculation of these figures is based on the number of notary transactions that are registered every month by the Dutch Land Registry Office (Kadaster). A revision of the figures is exceptional and occurs specifically if an error significantly exceeds the acceptable statistical margins. The average purchasing prices of existing owner-occupied sold homes can be calculated by Kadaster at a later date. These figures are usually the same as the publication on Statline, but in some periods they differ. Kadaster calculates the average purchasing prices based on the most recent data. These may have changed since the first publication. Statistics Netherlands uses figures from the first publication in accordance with the revision policy described above.

Changes as of 17 February 2025: Added average purchase prices of the municipalities for the year 2024.

When will new figures be published? New figures are published approximately one to three months after the period under review.

Real estate ads in Russia are published on the websites avito.ru, realty.yandex.ru, cian.ru, sob.ru, youla.ru, n1.ru, moyareklama.ru. The ads-api.ru service allows you to upload real estate ads for a fee. The parser of the service works strangely and duplicates real estate ads in the database if the authors extended them after some time. Also in the Russian market there are a lot of outbids (bad realtors) who steal ads and publish them on their own behalf. Before publishing this dataset, my task was to select the original ad from a bunch of ads. Russian real estate services allow ad authors to manually write data about an apartment or house. Therefore, it often happens that a user can publish an ad with errors or typos. Also, the user may not know, for example, the type of walls near his house. The user also specifies the address of the object being sold. He may make a mistake and simply indicate the address, for example, "Moscow". Which street? Which house? We will never know.

Dataset

The real estate market in Russia is of two types, in the dataset it is used as object type 0 - Secondary real estate market; 2 - New building. I found it necessary to determine the geolocation for each ad address and add the coordinates to this dataset. Also there is a number of the region of Russia. For example, the number of the Chuvash region is 21. Additionally, there is a house number that is synchronized through the federal public database of the Federal Tax Service "FIAS". Since the data is obtained through a paid third party service, I cannot publish the results, however, I can anonymize them and publish parameters such as Street ID and House ID. Basically, all houses are built from blocks such as brick, wood, panel and others. I marked them with numbers: building type - 0 - Don't know. 1 - Other. 2 - panel. 3 - Monolithic. 4 - Brick. 5 - blocky. 6- Wooden

The number of rooms can also be as 1, 2 or more. However, there is a type of apartment that is called a studio apartment. I've labeled them "-1".

Ideas

I hope that the publication of this dataset will improve developments in the field of global real estate. You can create apartment price forecasts. You can analyze real estate markets. You can understand that there is a need to publish free real estate datasets. And much more

Others

The license for this dataset is public, you can use it in your scientific research, design work and other works. The only condition is the publication of a link to this dataset. You can send suggestions (or complaints) on the dataset by mail daniilakk@gmail.com

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.

This dataset contains material volumes (m³), material masses (kg), and material intensities (kg/m²) for representative buildings from 45 residential building cohorts in Finland. These data are presented per material and in total, aggregated on three hierarchical levels on the correspondingly named sheets in the OpenDocument Spreadsheet (ODS) file: the entire building (data_building), distinguished between vertical building levels (data_building_level), and distinguished between building parts (data_building part). Further details on the data are provided on the description sheet in the same file.

The cohorts are based on building type, main frame material, main façade material, and construction decade. Each cohort is represented by one inventoried building, covering the combinations in the tables below. All buildings are located in the city of Vantaa, Finland, with the exception of the 1940s and 1950s houses, which are based on type-planned houses and thus have no specific location.

One dwelling house cohorts included in the dataset

Frame material, Facade material	1940s	1950s	1960s	1970s	1980s	1990s	2000s	2010s
Wood, Wood	D	D	D	T	D	D	D	D
Wood, Brick		T	T	D	T	T	T	T
Brick, Brick			D	D	D*	D	D	D
Concrete, Concrete			D	T	D	D	D	D
Concrete, Brick			T	D	T	T	T	T

Block of flats cohorts included in the dataset

Frame material, Facade material	1940s	1950s	1960s	1970s	1980s	1990s	2000s	2010s
Concrete, Concrete			D	D	D	D	D	D
Concrete, Brick			T	T	T	T	T	T

D = Direct record based on construction documents.
T = Theoretical variant with alternative façade material based on typical contemporary construction practice. All properties except the cladding and any related materials (e.g. battens) are identical with the corresponding direct record.
* Geometry determined based on construction documents from 1979 due to lack of suitably sized cases dated in the 1980s. Insulation thicknesses adjusted to match the represented decade’s building code.

The data are primarily based on digitized construction documents obtained from the Vantaa building inspection authority’s archives through the purchase portal Lupapiste Kauppa (https://kauppa.lupapiste.fi/). The 1940s and 1950s type-planned houses’ drawings are from the National Archives of Finland.

This dataset provides a comprehensive collection of features related to houses in California, with the primary aim of facilitating the prediction of house rent prices. It includes 80 columns and 1460 rows, offering a rich set of information for model training and evaluation. Target Variable: The dataset aims to predict the house rent prices, making it suitable for regression models. The 'SalePrice' column can be used as the target variable for training and evaluating predictive models.

Columns:

1. Id: Unique identifier for each record.
2. MSSubClass: The building class
3. MSZoning: The general zoning classification of the property.
4. LotFrontage: Linear feet of street connected to property.
5. LotArea: Lot size in square feet.
6. Street: Type of road access to property.
7. Alley: Type of alley access to property.
8. LotShape: General shape of the property.
9. LandContour: Flatness of the property. ... (and many more)

Use Case: Ideal for exploring and implementing regression models, particularly Linear Regression, to predict house rent prices based on various features associated with the properties.

Dataset Size: 80 columns 1460 rows

Source: This dataset is based on houses in California, making it relevant for studying the factors influencing house rent prices in this region.

Note: Please refer to the dataset documentation for details on each column and additional information regarding the data. Feel free to use this dataset for your machine learning projects, research, or educational purposes. Happy coding!

This dataset contains Iowa households by household type for State of Iowa, individual Iowa counties, Iowa places and census tracts within Iowa. Data is from the American Community Survey, Five Year Estimates, Table B11001. A household includes all the persons who occupy a housing unit as their usual place of residence. A housing unit is a house, an apartment, a mobile home, a group of rooms, or a single room that is occupied as separate living quarters. Household type includes All, All Family, Family - Married Couple, Family - All Single Householders, Family - Male Householder - No Wife Present, Family - Female Householder - No Husband Present, All Nonfamily, Nonfamily - Householder Living Alone, and Nonfamily - Householder Not Living Alone A family household is a household maintained by a householder who is in a family. A family group is defined as any two or more people residing together, and related by birth, marriage, or adoption. Householder refers to the person (or one of the people) in whose name the housing unit is owned or rented (maintained) or, if there is no such person, any adult member, excluding roomers, boarders, or paid employees. If the house is owned or rented jointly by a married couple, the householder may be either the husband or the wife.

Average house prices are derived from data supplied by the mortgage lending agencies on loans approved by them rather than loans paid. In comparing house prices figures from one period to another, account should be taken of the fact that changes in the mix of houses (incl apartments) will affect the average figures. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. Excluding apartments, measured in € Figure changed on the 27/6/16 as revised data received from the Local authority

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.

Local authority ESB Connections do not include second-hand houses acquired by them. New units acquired under Part V, Planning & Development Acts 2000-2008 for local authority rental purposes are included. Voluntary & co-operative housing consists of housing provided under the capital loan & subsidy and capital assistance schemes. Data on this variable was not available until 1993. ESB Connections data series are based on the number of new dwellings connected by ESB Networks to the electricity supply and may not accord precisely with local authority boundaries. These represent the number of homes completed and available, and do not reflect any work-in progress. Direct comparisons cannot be made with 2006, as those figures included some units built in 2005. ESB Networks have indicated that there was a higher backlog in work-in-progress in 2005 than usual ( estimated as being in the region of 5,000 units). This backlog was cleared through the connection of an additional 2,000 houses in Quarter 1 2006 and 3,000 houses in Quarter 2 2006. Due to circumstances beyond the Department's control it has not been possible to obtain a separate set of figures for the first two quarters of 2005. Direct comparisons cannot be made between pre 2009 and post 2010 data onwards. Up to 2010, completions relating to long term voids and demountables were included as new build completions. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. 2010 figure for Social Housing-Voluntary & Co-operative Housing; Malcolm Hillis - (DECLG): changed from 741 to 753 as 12 units omitted from original 2010 figures 18/11/15 2015 figure for Social Housing – LA Housing; This was previously 64. It was changed on the 27-4-16 when revised data was received by the Department. .hidden { display: none }

The Office of Consumer Protection (OCP) licenses builders or anyone acting in the capacity of a building contractor who constructs new homes in Montgomery County. This data consists of all active new home builder license holders. OCP does not license home improvement (ex. repair, remodeling, partial replacement, addition, or modernization, of existing structure) contractors; these contractors are licensed by Maryland Home Improvement Commission. The license information is deemed to be reliable, but we cannot guarantee the accuracy and completeness of the information. Any information that is shown to be inaccurate will be corrected if brought to the attention of OCP. Data Update Frequency : Daily

The number of dwellings by dwelling occupancy, shared dwellings, accommodation type, tenure, central heating type and number of bedrooms. Data are available at country, region, local authority, Middle layer Super Output Area and Lower layer Super Output Area in England and Wales, where possible.

Number of residential property sales in England and Wales, by property type and electoral ward. Annual data.

Average house prices are derived from data supplied by the mortgage lending agencies on loans approved by them rather than loans paid. In comparing house prices figures from one period to another, account should be taken of the fact that changes in the mix of houses (incl apartments) will affect the average figures. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. Excluding apartments, measured in € Figure changed on the 27/6/16 as revised data received from the Local authority .hidden { display: none }

Development of the National Register for Social Housing (NROSH) was started by the Department for Communities and Local Government (DCLG) in 2004. NROSH aimed to be a database of all social housing properties in England, with a range of details captured on each property. NROSH was transferred to the Tenant Services Authority, the social housing regulator, in April 2010 and was discontinued in May 2011. Ownership of the latest NROSH dataset passed from the TSA to the Homes and Communities Agency (HCA) when responsibility for social housing regulation passed to the Regulation Committee of the HCA in April 2012. In addition to being out of date, the records submitted by social landlords to NROSH are of varying quantity and quality with many incomplete, inaccurate or missing records. The database may also contain a number of duplicate entries. Two datasets are available. One is the latest NROSH database held by the HCA as at May 2011. This release contains a large subset of the full NROSH dataset (48 from 201 fields in total; for 4,826,417 unique property records). The data in this release does not include those fields where data could enable specific identification of vulnerable people or other sensitive personal data. It also excludes fields where a minimum completion threshold is not met (generally fields where less than 25% of records have data). There are still issues of quality, incomplete data, and potential duplication of records in the data that accompanies this release that HCA is not able to resolve. Additional information, including data that falls below the minimum quality thresholds for this release, may be requested from the HCA (Referrals & Regulatory Enquiries Team, mail@homesandcommunities.co.uk). The 48 fields included in this release are summarised and described in the two tables accompanying this metadata. The data is contained in five compressed single CSV files: NROSH Data Extract Part 1; - 2; - 3; -4 and -5. Due to the large volume of records, analysis will require database software (MS Excel will not support analysis). Also available is a snapshot of the NROSH database held by DCLG as at March 2010. The data is that which was reported by social landlords in line with the system specifications and includes a selected set of fields on property address, type of accommodation, form of structure, number of rooms and bedspaces are included.

Real estate datasets from various websites cover all major real estate data points including: property type, size, location, price, bedrooms, baths, address, history, images, and much more. Popular use cases include: forecast housing demand, analyze price fluctuations, improve customer satisfaction, see past prices to monitor market trends, and more.