Custom real estate data analytics helps businesses to make decisions on their investment portfolio management, buying or selling properties, manage real estate agents and learn more about their market.
The process involves automatically extracting the data from relevant sources and visualising it to clearly see the patterns in data. Understanding these patterns helps real estate investors, agents and property managers to inform decisions about their strategies.
In this article, we will firstly explore the benefits of real estate data analytics, secondly discuss its use cases, and thirdly analyse what software can be used for real estate data analytics.
We will explain this topic based on 4 different projects that we delivered as a data analytics consultancy:
Many investors are interested in real estate as a source of passive income. However, low management involvement without proper management reporting may be dangerous. This may lead to poor financial management, lower return on investment and harm the business reputation.
Real estate analytics allows you to track the performance of your property portfolio in real-time. Analysing Portfolio Performance metrics such as occupancy rates, rental income and maintenance costs allow you to identify underperforming assets and make informed decisions to optimise returns.
Each real estate property is a separate business. As such the data on different property performance is often segregated. We have seen cases where 100+ properties were in different QuickBooks Online accounts.
Consolidating data from multiple properties can be overwhelming and time-consuming. However, using appropriate data analytics tools helps to consolidate data into a single report, providing a structured portfolio overview.
Real estate agencies often struggle to measure agent performance. Creating structured data analysis enables managers to track performance of individual agents and helps to manage the team better.
It is common in real estate agencies to analyse metrics like sales volume, lead conversion rates and client satisfaction. This analysis helps to manage resources more effectively, identify top performers, allocate training and improve productivity.
Analysing the data on real estate market is an essential part of informing the investment decisions. It helps to understand supply/demand mix of homes in investment areas, form expectations on how long on average it takes to buy/sell a property and understand the average property price in an area.
Analysing market trends like property prices, demand-supply dynamics and demographic shifts help to ensure that profitable investments are made. It also enables the real estate agents to better consult their clients on buy/sell decisions.
In 2023 we worked with a commercial real estate company that lends storage units. Each unit had its data in a separate QuickBooks Online account and the company totalled more than 100 QuickBooks accounts. Understandably it was a challenge to consolidate all the data together.
We used a QuickBooks Online connector to extract the data from all these accounts automatically and combine it all in an Azure SQL Server database. We then created a Power BI dashboard with an account filter. As a result, the client could filter the analysis to one particular unit or see them all together.
No business can run without a profit and loss statement. This analysis helps real estate companies to monitor profitability, income and expenses over time. NOI is another essential commercial real estate metric to keep an eye on. It measures income from a property minus all the operating expenses and is an essential profitability metric.
This real estate analytics report helped our client to identify trends, inefficiencies and optimise resource allocation for profitability and growth.
Given that taking loans and mortgages is common in real estate, it is crucial that real estate agents monitor assets-to-liabilities ratio. This ratio helps agents evaluate a company’s financial health and its ability to repay its debts.
Using these real estate analytics metrics, our client got a detailed analysis of its liabilities portfolio that was divided into groups. A company can easily compare its assets against liability and perform an accurate evaluation of its financial health.
A common data analytics challenge in residential real estate is measuring the profitability trend for each property. For example our recent client invested in 30 residential properties and needed a clear view of revenue, expenses and profit metrics to ensure effective financial management and positive cash flow.
The analysis that we created allowed the client to see profit margin by property to understand the most profitable assets in the portfolio.
We also analysed expenses by month to ensure that they are staying stable. If we saw any spikes, we used the line chart on the right to identify which specific expense caused the spike.
This real estate data analytics report also offers a way to filter to a specific property and see the monthly rent and expenses trends. This analysis helps to identify vacancies that need to be addressed and unexpected expenses that need to be budgeted for in the future.
The second page analyses the planned vs actual performance of every property. This page allows the real estate company to quickly see which whether the rent and expenses are in line with expectations.
The table on the top right highlights the expenses that are over budget in red whereas the expenses within the budget are in blue. If we noticed that the expenses for a certain property are consistently over budget, we communicated this to the property managers to find ways to reduce them.
Data analytics in real estate agencies is usually focused around measuring agent performance. This performance is usually measured as number of sales, sales value, number of processed referrals, completed tasks, etc.
When we worked with Realogy (Fortune 100 real estate agency), we extracted the data on real estate agent activity from their Salesforce account and created a dashboard for the managers to help them effectively manage their team.
The first page of the real estate agent performance report provides detailed analysis of total received referrals, conversion rates and tasks progress. This real estate analysis helps the client to evaluate overall performance, allowing to identify trends, inefficiencies and opportunities for improvements. The company can improve overall productivity and agent effectiveness by optimising referral assignment and task management.
The second page of real estate analytics report helps to analyse the lead sources and identify the ones that result in most buy/sell deals. We also analysed the number of active, pending and won deals by every lead source.
Finally, it is important to analyse the local market before making a real estate investment decision or consulting clients. Understanding market trends is essential to build expectations on whether the price of your investment will increase and how easy it would be to sell it in the future.
We previously created this analysis for a US-based real estate agency. As part of it we automatically extracted the real estate market data from MLS Grid API. We then visualised this data in Looker Studio to highlight market trends.
It is useful to think about the real estate market analysis as trying to understand supply vs demand in your area.
Demand-side metrics would be average home sale price, average time to sell, etc.
Supply-side metrics would be inventory (number of properties on the market right now), listed home price, etc. It is also useful to analyse what kind of properties are listed on the market: how many bedrooms they have, what is their type and postcode.
This analysis helps to understand the average price for every type of home which effectively informs the decisions on making or accepting an offer for a house. It also helps to build expectations for how long it would take to buy or sell a house in a certain area.
It is also useful to analyse new listings (number of homes newly added to the market) and listings under contract (listings that are taken off the market). This helps to measure the rate of market changes within an area that you analyse.
Absorption rate is calculated as number of monthly home sales vs total number of available properties. Absorption rate of 20%+ is a sign that the area is a seller’s market whereas when it is below 15%, it is a sign of a buyer’s market.
Real estate market analysis is also largely about the average number of days that properties are on the market. The charts on left side of the page above help our client find which property types are easier to sell in a selected market.
The analysis on the listing price reduction helps the real estate investors understand the power of buyers in a particular market. The graphs that we produced above help our client to understand the average time to discount, average discount type, which property types are discounted, etc.
We are big believers in custom real estate data analytics since every business is unique. Because of this we recommend that the data analytics software that you pick allows you to create custom analysis for your own business rather than gives you a bunch or pre-created charts.
We are big advocates for BI tools like Power BI, Tableau and Looker Studio. These tools enable you to analyse the financial data of your real estate company and create actionable management reports.
Optionally you may want to use data engineering services to automatically extract the data from your cloud-based data sources. Data engineering platforms like Azure or Google Cloud Platform are the best fit for these tasks in our experience.
Finally, some real estate companies that we have worked stored their data on the following data sources:
Our data analytics consultants have delivered 1000+ projects for 600+ international clients. We have significant experience in data analytics and the real estate market in general. We would love to help you with your next project! Contact us today!
