Predictive analytics real estate: Top 5 use cases

predictive analytics real estate
predictive analytics real estate

While Artificial Intelligence and Machine Learning (AI/ML) is gaining relevance in real estate, predictive analytics is also disrupting the market in several ways. From value prediction and property search to portfolio management and predictive maintenance, predictive analytics real estate is instrumental in implementing non-traditional variables of underlying data to churn out data-driven insights for accurate decision-making.

Earlier investors restricted themselves to limited financial factors. They relied on industry experience and their intuition to evaluate and predict the potential of sophisticated investments and market trends. With the advent of Predictive analytics in real estate, they can access granular data to forecast macroeconomic market trends with increased accuracy.

What is Predictive analytics real estate?

Predictive analytics algorithms utilise several underlying factors. It combines historical data, behavioural analytics, market indicators, and real-time data. As a result, predictive analytics develops highly-tailored solutions for consumers.

Statistics, data mining, and machine learning constitute predictive modelling techniques. Therefore, in simple terms, predictive analytics amalgamate past and real-time data with analytics to produce future insights.

In the context of real estate, predictive analytics can prove functional in operational procedures, real estate transactions and other use cases. The advanced programming algorithms of AI/ML and predictive analytics in real estate can help in identifying patterns and signals that realtors may miss out on due to their access to conventional datasets.

Real estate professionals harness the power of data analytics to determine market conditions in the future. These include rent, property values, occupancy rates, and return rates in the future. Furthermore, real estate professionals can advise investors to respond faster to emerging market trends and prevent future exigencies.

In a survey by Deloitte, more than 80% of investors wanted commercial real estate businesses to enforce Predictive analytics. This, they believed, could both accelerate and streamline operations. Moreover, prioritizing data analytics and business intelligence could enhance operational efficiency, improve customer service, and generate higher revenues. 

Top 5 use cases of predictive analytics real estate

1. Improved investment recommendations

In real estate, successful investments require quicker decisions that rely on accurate data. However, the exponentially growing number of data points makes it impossible for investors to process such critical data in near real-time.

However predictive analytics in real estate makes it easier for investors to find the perfect investment property. 

For instance, location plays an important factor in making an investment property a lucrative one. Predictive analytics algorithms collect, organise and analyse specific data for a particular area. 

With predictive analytics tools like heatmap analysis, investors can identify the ideal locations for rental property investments. So, they can generate maximum profits. 

Heat map analysis identifies the most suitable areas for investments. It bases its selection on metrics such as property value, rental income, and occupancy rates.

In addition to this, predictive analytics in commercial real estate can provide property recommendations to investors. And, these recommendations are based on historical data and future market investment trends. 

Big data can potentially estimate how the property values, occupancy rates and rents in the properties of a particular neighbourhood/area will change in the future.

Also, real estate investors can get actionable insights into the type of investments that generate the maximum returns on investment. 

For instance, Real estate Investment Trusts (REITs) can utilize macro (Funds from Operations) and micro-business specific factors (parking, proximity to commercial, business and social hubs in the vicinity). As a result, they can identify the most profitable asset for stable income generation.

Therefore, even non-experienced investors can use this information for negotiating lease renewals and rental rates.

2. Estimating property investment values

In the end, real estate investments come down to accurately estimating an investment property’s value. Traditionally, real estate agents and brokers consider conventional data to define the selling price of an asset. These include parameters like location, type of neighbourhood and target audience.

However, predictive analytics recognizes the potential of non-traditional data sets for asset value prediction. Therefore, predictive analytics in real estate curates data-driven, smarter valuation models. 

Predictive analytics combines conventional data like vacancy and occupancy rates and several-minute alternative metrics. These include access to transportation and the quality and distance of points of interest in the area. 

These include the asset’s proximity to a 5-star hotel, the average rating of nearby pubs and a number of gas stations and cafés within a mile.

Moreover, investors can use predictive analytics to estimate the market performance and median income of households in an up-and-coming area. 

Therefore, real estate investors can identify the job opportunities, crime rates, schools, spending patterns of the inhabitants, their commuting habits and so on. They can also locate the neighbourhoods where the affluent and middle-income groups reside.

Thus, in accordance with this data, investors can estimate property investment values that align the most with market trends.

Furthermore, advanced predictive analytics-based forecasting tools help investors to leverage data about shifting labour markets. This is particularly useful for business leaders and larger firms who can plan for their current and future employment needs. With an extensive predictive analytics model, these leaders get an accurate idea of specific worker types in a region. So, they can accelerate their site selection process.

3. Portfolio management

In real estate, predictive analytics can prove particularly useful in managing expansive and evolving real estate portfolios.

Predictive analytics commercial real estate recommends portfolio managers smarter strategies for portfolio expansion. It combines data from both conventional and non-conventional sources. So, it provides actionable insights that are capable of forecasting the performances of assets within a portfolio.

For instance, performance data sets of hotels can be derived from data obtained from social media and the travelling patterns of people in the adjoining areas.

Furthermore, with the help of predictive analysis, real estate portfolio managers can utilise user data to understand a client‘s journey in a detailed way. These real estate professionals can segment their clients into different types based on their historic behaviour. 

As a result, they can determine whether these clients are first-time buyers, savvy investors, or property owners looking to sell or rent out their property and so on.

As a result, different segments of clients will show different preferences, motivations and behaviours. Thus, portfolio managers and real estate agents can capitalise on predictive recommendations to offer a custom-made experience for each entity.

In addition to this, predictive analytics in commercial Real estate enables portfolio managers to identify the points of risk. A thorough analysis of macroeconomic conditions or regulatory changes in a given market enables portfolio managers to identify risk sources.

Therefore, data and predictive analytics guide portfolio managers to a wide range of strategies. These include guidance for potential investments, portfolio analysis and expansion strategies, and managing the properties within a portfolio.

4. Predictive maintenance

A combination of Machine learning and IoT data analytics enables property managers to have a detailed insight into future maintenance issues of an asset.

Predictive analytics and IoT data analytics use real-time data and statistical modelling to create smart buildings and better understand energy consumption patterns and optimize resource efficiency.

With predictive analytics, property managers can implement a ‘what-if’ Property Management based on tenant requests, contractor work and repair history.

In addition to this, predictive analytics examine data through smart sensors that various electronic equipment generate. Therefore, it can detect anomalies in temperature, pressure, vibration, air quality, sound and humidity even before they start to malfunction.

Also, predictive maintenance commercial real estate gathers data from wi-Fi and IOT sensors. Then an AI-based algorithm analyses these data and turns them into actionable insights which optimize building maintenance. 

Therefore, predictive analytics helps in setting smarter controls for Heating Ventilation and Air-conditioning (HVAC), Lighting control, energy efficiency, and occupant tracking. 

Hence, while gathering data from alternative data sets predictive analytics commercial real estate Reduces operating costs, improves the living experience, and boosts workplace productivity.

Also, with the help of spatial analysis, property managers can get data-driven insights on building renovation/remodelling. 

Moreover, intuitive predictive analytics tools analyze the upgrades and improvements of local properties. As a result, developers can get a better idea of what specific amenities and upgrades Investors are willing to pay more for what they expect at a given price. Thus, Real estate professionals can boost their ROI.

5. Targeted marketing

The main objective of marketing campaigns is to identify potential buyers and sellers accurately. However, in real estate, Google or Facebook ads and marketing emails were the most effective solutions.

But, with the emergence of predictive analytics models, real estate agents can curate targeted marketing campaigns with data-driven insights into buyer/seller preferences and behaviours.

For instance, predictive analytics collects much of its data from The social media behaviour of consumers. Moreover, it also analyses various other consumer data Like Google search history, light force and social media, and recent further changes. 

Therefore, if a particular consumer repeatedly purchases tools for home renovation and has searched for similar information on the internet, it often means that they intend to sell their property at a higher price. Hence, real estate search portals can connect interested buyers who want to purchase a value-added property with such sellers.

Additionally, predictive property matching can match buyers with assets that match their lifestyle patterns instead of only their search inputs. As a result, real estate firms can optimize their marketing campaigns. Buyers can now access new and improved properties in up-and-coming neighbourhoods that were previously inaccessible. 

Predictive analytics real estate: The Lilypads Bottomline

Commercial and residential real estate is rapidly changing with growing advancements in artificial intelligence, machine learning, and predictive analytics applications. 

As real estate continues to generate exorbitant amounts of data, by putting predictive analytics to effective use, real estate industry players can utilise the underlying insights from seemingly unrelated alternative data points. Hence, newer real estate investors and professionals can optimize their operations with needed data-driven insights and accuracy. 

AI in commercial real estate coupled with predictive analytics and machine learning brings a novel sophistication to how investors are working towards reeling their long-term business and investment objectives.

Thus, predictive analytics commercial real estate makes sure that the industry players in a digital landscape are adopting technological innovations to keep up with the evolving consumer patterns.

Therefore, whether it is making smarter decisions in investments, stimulating profitable investment opportunities, portfolio management, evaluating investments or predictive maintenance- it is evident that moving forward, predictive analytics will be a powerful driver of the real estate industry.

Read More blogs from Lilypads here:

  1. AI in commercial real estate: the face of digital disruption
  2. AI in commercial lending: the future of automation and change