Real estate data analytics has transformed the industry, giving real estate investors an important tool for examining large data sets so they can understand what the data means and respond faster to market trends.1 Traditionally, real estate has been a speculative investment—buying a property when the value drops and selling it when the market recovers. This may mean purchasing a property that requires renovations, in a location that you believe will see future development. However, this is risky, as it usually involves making a prediction based on limited research and anecdotal evidence.2
With predictive analytics, real estate investors can make better-informed strategic decisions for greater success. This is clearly the way of the future. In KPMG’s 2018 Global PropTech Survey, almost half of the respondents believed that big data, data analysis and artificial intelligence (AI), of all the new technologies, would have the most significant long-term impact on the real estate industry.3
Keep reading to learn how investors can use real estate data analytics to gain market insights for a competitive advantage.
What is Real Estate Data Analytics?
Real estate analytics examines large amounts of raw data to make determinations based on the information that the data contain. The term big data refers to large and highly diverse sets of information that are collected at a very fast speed.4 To make the best use of the huge amount of information that is available, savvy real estate investors most often use specialized software that compiles data from government and public databases, surveys of consumers and businesses and other online information that the software can collate.1
How Does Real Estate Use Data Analytics?
Real estate analytics enable investors to work with large raw datasets to extract valuable insights. For example, trying to average historical performance data of property transactions is complex, since no two properties are the same. Data analytics is a game-changer for real estate investors.6
Real estate professionals can use data analytics for:
Property Price Indices
Data analytics can look at the value of each property characteristic separately or compare prices on properties that sell multiple times. It can combine large quantities of data with information such as property characteristics, locality and demographics, producing highly detailed submarket indices. Seeing historical trends helps in pricing and estimating future returns.6
Automated Valuation Models
Investors use data to estimate a property’s market value. This uses a similar approach as indexation, but with more advanced machine learning and ensemble techniques. An ensemble method combines base models to produce a single predictive model. The result is an estimate of the asset’s value, as a point or a range (rather than an index). This gives investors an instant, low-cost and current fair market value for a property.6,7
Time Series Forecasting
This method shows where property markets will be in the future, for smarter investment and development decisions with greater returns. Autoregressive Integrated Moving Average (ARIMA) models use a single set of data to make short-term predictions. Vector Autoregression (VAR) and Vector Error Correction Models (VECM) use property price indices for different yet related markets, plus other financial indicators like mortgage and interest rates. It might also use variables such as unemployment and inflation rates to create a more accurate prediction of future property prices.6
Real estate markets can be affected by changes in government legislation and other factors such as economic activity. With cluster analysis, real estate investors can see patterns in the data, which shows groups of properties that will likely perform similarly, and those that diverge. This type of analysis is also used to see the time periods when market performance may be similar, and to build models for a period or group, improving accuracy. Cluster analysis helps guide a company or investor’s business strategy.6
Geographic Information Systems (GIS)
Location is a key factor in real estate analytics. There is an increasing number of publicly available government data sources, from the location of amenities to population migration down to neighborhoods. With GIS, it is possible to find properties that match certain criteria, determine commute times and find ideal store locations based on transportation and population characteristics.6
These are some of the practical uses of real estate analytics.
What Are the Four Types of Analytics Used in Real Estate?
There are four basic categories of real estate analytics that manipulate and look at data in different ways, for different purposes. The least complex and quickest to implement is descriptive analytics, but the most complex—prescriptive analytics—offers the greatest value.5
1. Descriptive Analytics
This is the easiest type of analytics to run, and it will describe what occurred over a certain period—that is, real estate trends. See how much rents have increased or decreased in a particular area or look at the previous year’s vacancy rates. Descriptive analytics examines various sources to gain helpful insight into past data.1
2. Diagnostic Analytics
Diagnostic analytics examines past data to explain, or diagnose, why something occurred. For example, an investor might use this form of analytics to learn why properties in a certain region have a high tenant or owner turnover rate. 1
3. Predictive Analytics
If you want to be able to predict what might happen in the future, you can generate forecasts from predictive data analytics. Using information from descriptive and diagnostic analytics, it identifies clusters, tendencies and exceptions for a comprehensive real estate market forecast. One example of this is to predict the rent increase rate in the next few years. 1
4. Prescriptive Analytics
As the first three data analytics types are to find out what happened, why it happened and what might happen in the future, prescriptive analytics prescribes what must be done to either avoid a problem or exploit a possibility. You might wish to invest in a multi-family home. This type of analytics can help you to determine how many bedrooms and bathrooms are most sought after. It can help you determine how to maximize the amount of rent you can ask for. This can be of great value for investors, but it’s the most complex type of analytics and involves tools such as business rules, machine learning and algorithms.1
How Data Analytics Drives Better Real Estate Investing
Using predictive analytics, real estate investors can respond more quickly to the latest trends and emerging markets.1 They can learn far more about a property’s potential financial return and risk, from extremely detailed data that includes everything from demographics, EPA reports and flood maps to tenants, traffic and surrounding retailers.8 With more data comes less uncertainty, lowering the risk associated with unknown variables—because nearly every variable is known and measured.
The Real Estate Data Analytics Skills Gap
Even with the obvious advantages of using real estate data analytics, as of 2019, only 20 percent of real estate firms said that most or all their decisions were guided by data. In KPMG’s Global PropTech Survey from October 2019, only five percent of companies surveyed had someone who specialized in data analytics leading their efforts toward digital transformation.9 This shows a serious skills gap and the need for real estate professionals who are trained in the latest data analytics techniques.
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- Retrieved on August 16, 2022, from mashvisor.com/blog/real-estate-data-analytics-get-rich/
- Retrieved on August 16, 2022, from realestateagent.com/real-estate-glossary/real-estate/real-estate-speculation.html
- Retrieved on August 16, 2022, from assets.kpmg/content/dam/kpmg/fr/pdf/2018/11/fr-KPMG-Global-PropTech-Survey-2018.pdf
- Retrieved on August 16, 2022, from investopedia.com/terms/b/big-data.asp
- Retrieved on August 16, 2022, from iterationinsights.com/article/where-to-start-with-the-4-types-of-analytics/
- Retrieved on August 16, 2022, from towardsdatascience.com/5-ways-to-apply-data-science-to-real-estate-e18cdcd0c1a6
- Retrieved on August 16, 2022, from towardsdatascience.com/ensemble-methods-in-machine-learning-what-are-they-and-why-use-them-68ec3f9fef5f
- Retrieved on August 16, 2022, from forbes.com/sites/forbesrealestatecouncil/2020/01/28/from-data-to-decision-data-analytics-will-drive-real-estate-transactions-in-the-future/?sh=64af87d96dd7
- Retrieved on August 16, 2022, from assets.kpmg/content/dam/kpmg/uk/pdf/2019/11/global-proptech-survey-2019.pdf