How to Use Predictive Analytics in Real Estate

Nobody can predict the future, but that doesn’t mean you shouldn’t try. In real estate, buying a property is feasible only when the investment can hold its value or appreciate, which takes some work to predict. 

Predictive analysis is the use of data to forecast future trends, property values, and investment opportunities in real estate. By analyzing historical information and current patterns, investors can make better decisions about where and when to buy, sell, or develop properties.

While predictive analytics isn’t exactly a crystal ball, using data to project past trends into the future can give you a glimpse of what’s likely to happen down the road. This can include predicting city growth, spotting high-demand neighborhoods, or even evaluating the impact of economic changes on property values.

In this blog, we’re talking about how predictive analytics can inform your real estate investing strategy. The insights of a data-driven prediction approach can help you predict demand in the coming years, reducing your financial risk and increasing your portfolio’s overall profitability.

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What is predictive analytics in real estate?

Predictive analytics is the process of leveraging past data to anticipate and make well-informed decisions about future outcomes.

Today’s predictive analytics relies on advanced tools and techniques such as:

• Machine learning algorithms
• Historical data and records
• Data mining processes
• Both current and historical statistical analyses

This powerful approach is applicable across various fields, but it’s especially valuable in scenarios requiring strategic planning real estate investing being a prime example.

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What are the three types of predictive analysis?

Predictive analysis isn’t a one-size-fits-all process. Some properties are easy to analyze while others take a little more thought. 

There are three key types of predictive analysis, and each one offers unique insights:

1. Descriptive analytics

Descriptive analytics is your typical, run-of-the-mill look at past and present data. It provides a snapshot of what’s happening and what has happened in a specific market or property type, which can give investors an overview of patterns and trends.

Example in action:
An investor looking at the historical growth of an area’s home prices over the past decade might find that property values increased steadily by 5% annually. This information sets the stage for further analysis.

2. Predictive analytics

Predictive analytics takes patterns from descriptive analysis and projects them into the future. It uses statistical models and algorithms to make educated guesses about what might happen next.

Example in action:
If data shows a correlation between population growth and housing demand, predictive analytics can estimate how much property values could rise in an area where population growth is accelerating.

3. Prescriptive analytics

Prescriptive analytics goes beyond predictions to recommend specific actions based on data insights. It answers the question of, “What should I do to achieve the best outcome?”

Example in action:
A real estate software tool might recommend that a developer invest in a parcel of land near a planned highway expansion, predicting significant value appreciation once the infrastructure is complete.

Examples of predictive analytics

If you’ve ever thought to yourself, “this seems like a nice place to live,” congratulations you’ve done predictive analysis on at least some level! 

Assessing an area’s livability is the first step to looking ahead at a property’s future value. 

Here are other examples of predictive analytics:

Spotting emerging neighborhoods

Investors use data to detect early indicators of neighborhood growth, such as an influx of new businesses, rising rental demand, or planned infrastructure projects. 

A real-life example of an emerging neighborhood was this decade’s transformation of Williamsburg, NY, where early investors used data to predict the neighborhood’s future as a cultural and economic hub.

Forecasting market trends

You can anticipate shifts in the real estate market by examining the rise or fall of property values, rental income trends, or even the popularity of specific property types like co-working spaces. 

During the COVID-19 pandemic, data showed a sharp rise in demand for suburban homes as remote work became widespread. Investors who jumped onto this trend early capitalized on increased suburban property values.

Assessing natural disaster risks

Natural disasters impact property values. Predictive analytics uses climate risk data to help investors avoid high-risk areas or budget for insurance costs.

In Florida, tools like FEMA’s flood risk data help to assess whether coastal properties are likely to face rising insurance premiums or even devaluation due to increasing flood risks.

Calculating predictive analysis

To calculate predictive analysis, you will need a combination of raw data and advanced tools in order to forecast outcomes. 

Let’s talk about how:

1. Gather data

Start by collecting diverse data sets, such as:

• Historical sales data: Past property prices, sales volume, and market activity
• Economic indicators: Employment rates, inflation, and GDP growth
• Demographics: Population growth, age distribution, and income levels
• Environmental data: Risks like flooding, wildfires, or earthquakes

2. Analyze trends

Find a way to visualize your data, such as with a tool like Tableau or Excel. For example, you might spot that areas with high job growth tend to see a spike in property values.

3. Build predictive models

Machine learning algorithms or statistical techniques can help you create forecasts. However, since most people don’t know how to use tools like Python or R, you may consider turnkey solutions to use data in predictive analysis.

4. Test your models

How do your predictions compare to real-world outcomes? Asking this can help you ensure your models are accurate.

It’s worth determining what you value in an investment property. Some investors are concerned with the short-term ability to rent out rooms, while others want a property that holds its value even though it doesn’t appreciate as quickly. These are helpful measurements for measuring a property’s potential.

What is a real estate KPI?

Real estate KPIs are Key PerformanceIndicators used to measure a property’s performance. They are how you can know whether a property or portfolio meets your financial goals.

Some common real estate KPIs might include:

• Net Operating Income (NOI): The revenue generated from a property minus operating expenses
• Cash-on-cash return: How much an investment returns vs. what it cost
• Internal Rate of Return (IRR): An investment’s total return over time
• Vacancy rate: The percentage of unoccupied rental units

By tracking these KPIs, investors can monitor the health of their investments and make adjustments as needed.

AI real estate tools

If only there were a tool that could use historical data to make helpful predictions…

Enter AI an algorithm tool that evaluates historical market conditions to offer useful recommendations. AI may not be foolproof, but it can enhance the accuracy and speed of predictive analysis. Tools like Leni and Opendoor analyze many different factors to inform your investment strategy.

The future of predictive analytics in real estate

The advancement of AI tools makes it clear that the future of predictive analytics in real estate looks brighter than ever. 

The Internet of Things (IoT) is expanding data collection opportunities, from smart home sensors to city-wide infrastructure monitoring. This real-time data will provide even more accurate forecasts.

Advances in AI and data analysis will also improve the ability to predict risk, from market downturns to climate-related hazards. This will enable investors to safeguard their portfolios more effectively.

Final thoughts

Predictive analysis is no longer a luxury it’s a necessity for modern real estate professionals. By leveraging the power of data, builders, developers, and investors can navigate the complexities of the market with confidence, ensuring smarter, more profitable decisions.

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