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

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.

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.

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

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.

Average House Prices in Australia increased to 1016.70 AUD Thousand in the second quarter of 2025 from 1002.50 AUD Thousand in the first quarter of 2025. This dataset includes a chart with historical data for Australia Mean Dwelling Price.

Australia Standardised Price-Income Ratio: sa data was reported at 149.268 Ratio in Dec 2024. This records a decrease from the previous number of 152.371 Ratio for Sep 2024. Australia Standardised Price-Income Ratio: sa data is updated quarterly, averaging 82.643 Ratio from Mar 1970 (Median) to Dec 2024, with 220 observations. The data reached an all-time high of 153.422 Ratio in Jun 2024 and a record low of 62.554 Ratio in Sep 1983. Australia Standardised Price-Income Ratio: sa data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Australia – Table AU.OECD.AHPI: House Price Index: Seasonally Adjusted: OECD Member: Quarterly. Nominal house prices divided by nominal disposable income per head. Net household disposable income is used. The population data come from the OECD national accounts database. The long-term average is calculated over the whole period available when the indicator begins after 1980 or after 1980 if the indicator is longer. This value is used as a reference value. The ratio is calculated by dividing the indicator source on this long-term average, and indexed to a reference value equal to 100.

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.

In the third quarter of 2023, the median price of residential property transfers for established housing in Melbourne came to around *** thousand Australian dollars. The highest median price of established housing transfers within the given time period was recorded in the fourth quarter of 2021.

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.

Urban housing location and locational amenities play an important role in median house price distribution and growth among the suburbs of many metropolitan cities in developed countries, such as Australia. In particular, distance from the central business district (CBD) and access to the transport network plays a vital role in house price distribution and growth over various suburbs in a city. However, Australian metropolitan cities have experienced increases in housing prices by up to 120% over the last 20 years, and the growth pattern was different across all suburbs in a city, such as in Melbourne. Therefore, this study examines the impacts of locational amenities on house price changes across various suburbs in Melbourne over the three census periods of 2006, 2011, and 2016, and suggests some strategic guidelines to improve the availability and accessibility of locational amenities in the suburbs with less concentrated amenities. This study chose three Local Government Areas (LGAs) of Maribyrnong, Brimbank and Wyndham in Melbourne. Each LGA has been selected as a case study because many low-income people live in these LGAs’ areas. Further, some suburbs of these LGAs have maintained similar housing prices for an extended time, while some have not.The study applied a quantitative spatial methodology to examine the housing price distribution and growth patterns by evaluating the concentration and accessibility of locational urban amenities using GIS-based techniques and a spatial data set. The spatial data analyses were performed by spatial statistics methods to measure central tendency, Local Moran’s I of LISA clustering, Kernel Density Estimation (KDE), Kernel Density Smoothing (KDS). These tests were used to find the patterns of house price distribution and growth. The study also identified the accessibility of amenities in relation to median house price distribution and growth. Spatial Autoregressive Regression (SAR), Spatial Lag, and Spatial Errors models were used to identify the spatial dependencies to test the statistical significance between the median house price and the concentration and access of local urban amenities over the three census years.This study found three median house price distribution and growth patterns among the suburbs in the three selected LGAs. There are growth differences in the median house price for different census years between 2006 and 2011, 2011 and 2016, and 2006 and 2016. The Low-High (LH) median house price distribution clusters between 2006 and 2011 became High-High (HH) clusters between the census years 2011 and 2016, and 2006 and 2016. The median house price growth rate increased significantly in the census years between 2006 and 2011. Most of the HH median house price distribution and growth clusters’ tendencies were closer to the Melbourne CBD. On the other hand, the Low-Low (LL) distribution and growth clusters were closer to Melbourne’s periphery. The suburbs located further away had low access to amenities. The HH median house price clusters are located closer to stations and educational institutes. Better access to locational amenities led to more significant HH median house price clusters, as the median house price increased at an increasing rate between 2011 and 2016. The HH median house price clusters recorded more growth between 2006 and 2016. The suburbs with train stations had better access to most other locational amenities. Almost all HH median house price clusters had train stations with higher access to amenities.There was a consistent relationship between median house price distribution, growth patterns, and locational urban amenities. The spatial lag and spatial error model tests showed that between 2006 and 2011, and 2006 and 2016, there were differences in the amenities. Still, these did not affect the outcomes in observations, and were related only to immeasurable factors for some reason. Therefore, the higher house price in the neighbouring suburb could increase the price in that suburb. The research also found from the regression analysis that highly significant amenities confirming travel time to the CBD by bus, and distance to the CBD, were negatively related in all three previous census years. This negative relationship estimates that the house price growth is lower when the distance is longer. Due to this travel to the CBD by bus is not a popular option for households. The train stations are essential for high house price growth. The house price growth is low when homes are further away from train stations and workplaces.This thesis has three contributions. Firstly, it uses the Rational Choice Theory (RCT), providing a theoretical basis for analysing households’ mutually interdependent preferences of urban amenities that are found to regulate house price growth clusters. Secondly, the methodological contribution uses the GIS-defined cluster mapping and spatial statistics in queries and reasoning, measurements, transformations, descriptive summaries, optimisation, and hypothesis testing models between house price distribution and growth, and access to urban locational amenities. Thirdly, this research contributes to designing practical guidelines to identify local urban amenities for planning local area development.Overall, this thesis demonstrates that the median house price distribution and growth patterns are highly correlated with the concentration and accessibility of locational urban amenities among the suburbs in three selected LGAs in Melbourne over the three census years (i.e., 2006, 2011, and 2016). The findings bring to the fore the need for research at the local and state levels to identify specific amenities relevant to the middle-class house distribution strategy, which can be helpful for investors, estate agents, town planners, and builders as partners for effective local development. The future study might use social, psychological, and macroeconomic variables not considered or used in this research.

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.

The Toorak area was the most expensive housing suburb in Melbourne, Australia as of March 2025, with a median property value of around *** million Australian dollars. The Canterbury suburb had the next highest prices for housing, at around **** million dollars.

Australia Standardised Price-Rent Ratio: sa data was reported at 168.732 Ratio in Dec 2024. This records a decrease from the previous number of 171.919 Ratio for Sep 2024. Australia Standardised Price-Rent Ratio: sa data is updated quarterly, averaging 70.511 Ratio from Sep 1972 (Median) to Dec 2024, with 210 observations. The data reached an all-time high of 189.671 Ratio in Mar 2022 and a record low of 48.119 Ratio in Sep 1972. Australia Standardised Price-Rent Ratio: sa data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Australia – Table AU.OECD.AHPI: House Price Index: Seasonally Adjusted: OECD Member: Quarterly. Nominal house prices divided by rent price indices. The long-term average is calculated over the whole period available when the indicator begins after 1980 or after 1980 if the indicator is longer. This value is used as a reference value. The ratio is calculated by dividing the indicator source on this long-term average, and indexed to a reference value equal to 100.

• 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.

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.

Housing Inventory: Average Listing Price Year-Over-Year in Palm Bay-Melbourne-Titusville, FL (CBSA) was -3.43% in October of 2025, according to the United States Federal Reserve. Historically, Housing Inventory: Average Listing Price Year-Over-Year in Palm Bay-Melbourne-Titusville, FL (CBSA) reached a record high of 30.79 in February of 2022 and a record low of -9.96 in March of 2023. Trading Economics provides the current actual value, an historical data chart and related indicators for Housing Inventory: Average Listing Price Year-Over-Year in Palm Bay-Melbourne-Titusville, FL (CBSA) - last updated from the United States Federal Reserve on November of 2025.

Housing Inventory: Average Listing Price Month-Over-Month in Palm Bay-Melbourne-Titusville, FL (CBSA) was -0.85% in September of 2025, according to the United States Federal Reserve. Historically, Housing Inventory: Average Listing Price Month-Over-Month in Palm Bay-Melbourne-Titusville, FL (CBSA) reached a record high of 5.39 in June of 2021 and a record low of -5.41 in July of 2022. Trading Economics provides the current actual value, an historical data chart and related indicators for Housing Inventory: Average Listing Price Month-Over-Month in Palm Bay-Melbourne-Titusville, FL (CBSA) - last updated from the United States Federal Reserve on November of 2025.