Introduction:

A growing number of property management companies around the world are recognizing the benefits of data analytics. Analytics is a necessary element of any digital marketing strategy. Analyzing data patterns and trends is key to ensuring a company reaches the right customers and targets people in the right way. The property industry is one of the best examples of an industry that is using data analytics to its advantage. These companies have found it has led to much more effective marketing strategies that help them reach their target demographics. A team of researchers from Malaysia addressed the role of big data in property management in a globally renowned paper from Research Gate in November which was hugely appreciated by the world leaders in analytics.

What is analytics in property management?

Property data analytics is the process of examining raw data with the aim of making a conclusion on the information they contain. This analysis is usually done with the aid of specialized systems and software for real estate investors. Big data can be compiled from census figures, public or government databases, business or consumer surveys or collating information online. Data analytics can help real estate professionals enhance operational efficiency, increase revenues, improve customer service efforts, optimize marketing campaigns, and respond faster to emerging market trends. There are numerous paid and free academic courses out there that beginner and experienced investors can use to learn data analytic skills for finding the best investment properties.

Types of Property Management Data Analytics

Descriptive analytics – This kind of analytics describes what happened over a specific duration (real estate trends). For example, how much has rental income increased in the past five years? What was the vacancy rate in the past year? Descriptive analytics looks at data from a wide range of sources to derive useful insights into the past.

Diagnostic analytics – Here, historical data is examined to explain why something happened. For example, diagnostic analysis can dig deep to reveal why rental properties have a high turnover rate.

Predictive analytics – Just as the name suggests, predictive real estate data analytics forecasts what might happen in the future. It uses information from diagnostic and descriptive analytics to identify clusters, exceptions, and tendencies to make a housing market forecast. For example, an investor can use real estate predictive analytics to forecast how much rent is likely to increase in the next two years.

Prescriptive analytics – The main aim of this analysis is to prescribe what needs to be done to take advantage of an opportunity or avoid a problem in the future. For example, let's say someone wants to invest in an Airbnb property. Should they purchase a home with 2, 3 or 4 bedrooms? How many bathrooms should it have? Prescriptive Airbnb analytics will help figure out what needs to be done to boost occupancy rates, thus ensuring positive cash flow in the future. However, this kind of analysis uses modern technologies and real estate investment tools like algorithms, business rules, and machine learning, thus making it difficult to implement and manage.

Investors can use data analytics in property management in several ways as follows in order to gain profits from it.

Location: Data analytics offer important insights into the current trends in real estate investment. Investors can analyze locations based on variables such as traffic congestion, school ratings, life quality, and crime rates.

Additionally, real estate data can help an investor determine how profitable a location will be in terms of occupancy rates, average rental income, and even the ROI of the area. Being able to identify the best locations quickly allows investors to stay one step ahead of the competition in finding rental properties. It also saves them the trouble of carrying out lengthy searches using traditional research methods.

Pricing: Whether an investor is looking to sell or buy, price optimization is crucial for making money in real estate. Real estate investors need to understand the market they are operating within in order to make the best pricing decisions. However, pricing data analytics is not just restricted to selling and buying prices. It can also be applied to other facets of real estate such as pricing of rates and taxes, rental data and statistics, pricing of services and building prices.

Faster and More Accurate Appraisals

Getting an appraisal is very crucial before buying or selling an investment property. Real estate data analytics provides a large amount of accurate information about sales for comparable properties in the same area. Investment property analysis makes it easy for valuators to offer price estimations quickly to real estate investors.

Commercial Trends

Commercial real estate investment is usually more large scale and complex than residential property investment. Therefore, investing successfully in commercial property requires in-depth real estate data analytics on consumer trends. What are consumers looking for? Who are the competitors in the industry? Is there enough demand for a new shopping mall? Are parents looking for new schools? Is there a shortage of restaurants? Real estate data analytics will answer such questions and help investors make a more informed decision.

Competition Analysis

Real estate data analytics can help investors review the performance of their competitors in the real estate business. Who is selling the most? Which companies are considered the most trustworthy? What customer rating do they have? What marketing strategies are they using? Investors can also use data analytics for profits, sales figures, and consumer reviews to evaluate their own performance.

Tenant Targeting

Using predictive real estate data analytics, investors can determine who their tenants will be in the coming years. Is there a consistent demand for rental units in the neighbourhood? Will the property have tenants in the next 2-3 years? How will the composition of tenants change in the next 5 years? What target group should the landlord focus on (single professionals, young families, college students, etc)? Such data analysis makes it easier for investors to target their advertising appropriately.

Conclusion

Real estate markets are volatile, complicated, and always in change. Therefore, buyers and sellers in the housing market need real estate data analytics in order to make wise investment choices and maximize their return on investment. With data analysis, investors can objectively evaluate where and when to invest, as well as determine the accurate value of properties. Metrics such as desirability analytics, market cycle risks, and price forecasts are very important in such analytics.

References:

  1. https://www.mashvisor.com/blog/real-estate-data-analytics-get-rich/
  2. https://www.talleyproperties.com/blog/data-analytics/
  3. https://www.smartdatacollective.com/data-analytics/
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