As a Data scientist, who yearns to experiment, learn and explore different techniques applied in this field, one cannot overlook the importance of application of Exploratory Data Analysis on various datasets out there.

This housing dataset provides a thorough analysis of the current state of the housing market. It includes information on housing prices, availability, and key trends, allowing you to gain a better understanding of the market and make informed decisions. Whether you're a homebuyer, investor, or simply interested in the state of the housing market, this dataset has valuable insights to offer.

Sydney had the highest median house value compared to other capital cities in Australia as of April 2025, with a value of over **** million Australian dollars. Brisbane similarly had relatively high average residential housing values, passing Canberra and Melbourne to top the pricing markets for real estate across the country alongside Sydney. Housing affordability in Australia Throughout 2024, the average price of residential dwellings remained high across Australia, with several capital cities breaking price records. Rising house prices continue to be an issue for potential homeowners, with many low- and middle-income earners priced out of the market. In the fourth quarter of 2024, Australia’s house price-to-income ratio declined slightly to ***** index points. With the share of household income spent on mortgage repayments increasing alongside the disparity in supply and demand, inflating construction costs, and low borrowing capacity, the homeownership dream has become an unattainable prospect for the average person in Australia. Does the rental market offer better prospects? Renting for prolonged periods has become inevitable for many Australians due to the country’s largely inaccessible property ladder. However, record low vacancy rates and elevated median weekly house and unit rent prices within Australia’s rental market are making renting a less appealing prospect. In financial year 2024, households in the Greater Sydney metropolitan area reported spending around ** percent of their household income on rent.

Median prices for dwellings/townhouses, and apartments by their year of settlement for the City of Melbourne.

Graph and download economic data for Real Residential Property Prices for Australia (QAUR628BIS) from Q1 1970 to Q2 2025 about Australia, residential, HPI, housing, real, price index, indexes, and price.

The average price of Australian residential property has risen over the past ten years, and in June 2025, it reached over one million Australian dollars. Nonetheless, property experts in Australia have indicated that the country has been in a property bubble over the past decade, with some believing the market will collapse sometime in the near future. Property prices started declining in 2022; however, a gradual upward trend was witnessed throughout 2023, with minor fluctuations in 2024. Australian capital city price differences While the national average residential property price has exhibited growth, individual capital cities display diverse trends, highlighting the complexity of Australia’s property market. Sydney maintains its position as the most expensive residential property market across Australia's capital cities, with a median property value of approximately 1.19 million Australian dollars as of April 2025. Brisbane has emerged as an increasingly pricey capital city for residential property, surpassing both Canberra and Melbourne in median housing values. Notably, Perth experienced the most significant annual increase in its average residential property value, with a 10 percent increase from April 2024, despite being a comparably more affordable market. Hobart and Darwin remain the most affordable capital cities for residential properties in the country. Is the homeownership dream out of reach? The rise in property values coincides with the expansion of Australia's housing stock. In the June quarter of 2025, the number of residential dwellings reached around 11.37 million, representing an increase of about 53,600 dwellings from the previous quarter. However, this growth in housing supply does not necessarily translate to increased affordability or accessibility for many Australians. The country’s house prices remain largely disproportional to income, leaving the majority of low- and middle-income earners priced out of the market. Alongside this, elevated mortgage interest rates in recent years have made taking out a loan increasingly unappealing for many potential property owners, and the share of mortgage holders at risk of mortgage repayment stress has continued to climb.

Housing Index in Australia increased to 183.90 points in the fourth quarter of 2021 from 175.60 points in the third quarter of 2021. This dataset provides the latest reported value for - Australia House Price Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Median prices for dwellings/townhouses, and apartments by their year of sale for the City of Melbourne.

Graph and download economic data for All-Transactions House Price Index for Palm Bay-Melbourne-Titusville, FL (MSA) (ATNHPIUS37340Q) from Q4 1979 to Q2 2025 about Palm Bay, appraisers, FL, HPI, housing, price index, indexes, price, and USA.

The average price of Australian residential property has risen over the past ten years, and in December 2024, it reached 976,800 Australian dollars. Nonetheless, property experts in Australia have indicated that the country has been in a property bubble over the past decade, with some believing the market will collapse sometime in the near future. Property prices started declining in 2022; however, a gradual upward trend was witnessed throughout 2023, with minor fluctuations in 2024. Australian capital city price differences While the national average residential property price has exhibited growth, individual capital cities display diverse trends, highlighting the complexity of Australia’s property market. Sydney maintains its position as the most expensive residential property market across Australia's capital cities, with a median property value of approximately 1.19 million Australian dollars as of April 2025. Brisbane has emerged as an increasingly pricey capital city for residential property, surpassing both Canberra and Melbourne in median housing values. Notably, Perth experienced the most significant annual increase in its average residential property value, with a 10 percent increase from April 2024, despite being a comparably more affordable market. Hobart and Darwin remain the most affordable capital cities for residential properties in the country. Is the homeownership dream out of reach? The rise in property values coincides with the expansion of Australia's housing stock. In the December quarter of 2024, the number of residential dwellings reached around 11.29 million, representing an increase of about 53,200 dwellings from the previous quarter. However, this growth in housing supply does not necessarily translate to increased affordability or accessibility for many Australians. The country’s house prices remain largely disproportional to income, leaving the majority of low- and middle-income earners priced out of the market. Alongside this, elevated mortgage interest rates in recent years have made taking out a loan increasingly unappealing for many potential property owners, and the share of mortgage holders at risk of mortgage repayment stress has continued to climb.

Median prices for dwellings/townhouses, and apartments by their year of settlement for the City of Melbourne by CLUE Small area.

Melbourne Housing Market (Cleaned)

This dataset is a cleaned and enhanced version of the original Melbourne Housing Market dataset by Anthony Pino, licensed under CC BY-NC-SA 4.0. It has been preprocessed to facilitate exploratory data analysis and house price prediction modeling.

Key Improvements

1) Cleaned Missing Data: Removed missing and null values to ensure data integrity.

2) Outlier Removal: Eliminated unrealistic price and land size outliers to better reflect Melbourne's housing market.

3) Data Type Optimization: Converted Date and BuiltYear columns from float to appropriate datetime formats for easier analysis.

Acknowledgments

This dataset is derived from the original work by Anthony Pino, available here. Please credit the original source when using this dataset.

Key information about Australia Gold Production

• Australia Real Residential Property Price Index was reported at 134.815 2010=100 in Jun 2025.
• This records an increase from the previous number of 133.974 2010=100 for Mar 2025.
• Australia Real Residential Property Price Index data is updated quarterly, averaging 48.406 2010=100 from Mar 1970 to Jun 2025, with 222 observations.
• The data reached an all-time high of 141.875 2010=100 in Mar 2022 and a record low of 31.307 2010=100 in Mar 1970.
• Australia Real Residential Property Price Index data remains active status in CEIC and is reported by Bank for International Settlements.
• The data is categorized under World Trend Plus’s Association: Property Sector – Table RK.BIS.RPPI: Selected Real Residential Property Price Index: 2010=100: Quarterly. [COVID-19-IMPACT]

Real estate markets are of great importance for both local and international investors. Sydney and Melbourne are two dynamic markets where economic and social factors have significant impacts on property prices. Below is a detailed description of each feature:

1. Date: The date when the property was sold. This feature helps in understanding the temporal trends in property prices.
2. Price:The sale price of the property in USD. This is the target variable we aim to predict.
3. Bedrooms:The number of bedrooms in the property. Generally, properties with more bedrooms tend to have higher prices.
4. Bathrooms: The number of bathrooms in the property. Similar to bedrooms, more bathrooms can increase a property’s value.
5. Sqft Living: The size of the living area in square feet. Larger living areas are typically associated with higher property values.
6. Sqft Lot:The size of the lot in square feet. Larger lots may increase a property’s desirability and value.
7. Floors: The number of floors in the property. Properties with multiple floors may offer more living space and appeal.
8. Waterfront: A binary indicator (1 if the property has a waterfront view, 0 other- wise). Properties with waterfront views are often valued higher.
9. View: An index from 0 to 4 indicating the quality of the property’s view. Better views are likely to enhance a property’s value.
10. Condition: An index from 1 to 5 rating the condition of the property. Properties in better condition are typically worth more.
11. Sqft Above: The square footage of the property above the basement. This can help isolate the value contribution of above-ground space.
12. Sqft Basement: The square footage of the basement. Basements may add value depending on their usability.
13. Yr Built: The year the property was built. Older properties may have historical value, while newer ones may offer modern amenities.
14. Yr Renovated: The year the property was last renovated. Recent renovations can increase a property’s appeal and value.
15. Street: The street address of the property. This feature can be used to analyze location-specific price trends.
16. City: The city where the property is located. Different cities have distinct market dynamics.
17. Statezip: The state and zip code of the property. This feature provides regional context for the property.
18. Country: The country where the property is located. While this dataset focuses on properties in Australia, this feature is included for completeness.

If you like this dataset, please contribute by upvoting

• 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.

Explore Melbourne, FL rental market 2025. The average long-term prices $1,911 and short-term $2,205, with trends shaping housing in a city of 85,718 residents.

Graph and download economic data for Housing Inventory: Median Listing Price per Square Feet Year-Over-Year in Palm Bay-Melbourne-Titusville, FL (CBSA) (MEDLISPRIPERSQUFEEYY37340) from Jul 2017 to Oct 2025 about Palm Bay, square feet, FL, listing, median, price, and USA.

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Graph and download economic data for Housing Inventory: Average Listing Price in Palm Bay-Melbourne-Titusville, FL (CBSA) (AVELISPRI37340) from Jul 2016 to Oct 2025 about Palm Bay, average, FL, listing, price, and USA.

Data collected as part of the City of Melbourne's Census of Land Use and Employment (CLUE). The data covers the period 2002-2023. The dwelling data is based on the Council's property rates database, using a simplified classification schema of Residential Apartment, House/Townhouse and Student Apartment. The count of dwellings per residential building is shown.

For more information about CLUE see http://www.melbourne.vic.gov.au/clue

Context

There's a story behind every dataset and here's your opportunity to share yours.

ACTIVITIES

Follow the process below to develop a model that can be used by real estate companies and real estate agents to predict the price of a house.

1. Business Understanding -Conduct a literature review to understand the factors that determine the price of houses globally and locally. -Based on the dataset provided, formulate a business question to be answered through the analysis.

2. Data Understanding -The data in the dataset provided was collected through webs scrapping. Conduct further reading to understand the process of web scrapping, how it is conducted (methods and tools) and any ethical challenges related to it.

3. Data Preparation -Conduct a detailed exploratory analysis on the dataset. -Prepare the dataset for modeling -Identify the technique relevant for answering the business question stated above. -Ensure that the dataset meets all the assumptions of the technique identified. -Conduct preliminary feature selection by identifying the set of features that are likely to provide a model with good performance.

4. Modeling -Split the dataset into two; training set and validation set. With justifications, decide on the ratio of the training set to the validation set. -Generate the required model

5. Evaluation -Interpret the model in terms of its goodness of fit in predicting the price of houses. -Assume that the model is not good enough and then conduct further feature engineering or use any other model tuning strategies at your disposal to generate additional two instances of the model. -Settle on the best model instance and then re-interpret.

6. Implementation -Think of how the model can be implemented and used by real estate firms and agents. -Identify possible challenges of applying the model. -Recommendations on how the model can be improved in future