The rise of modern data analytics has had a transformative influence and a huge impact on the property sector. Data analytics is the process of analysing raw data with specialised systems and software to make conclusions about that information. For the real estate sector, and countless other industries, it can help a business optimise its performance. In this article, we’ll look at how data analytics Is used in the property sector and how it’s changing the way businesses within the industry behave.
Investors Can Use Data Analytics To Find:
● Location
● Pricing
● Faster and More Accurate Appraisals
● Commercial Trends
● Competition Analysis
● Tenant Targeting and more
Four Types of Real Estate Data Analytics
Descriptive Analytics Descriptive analytics looks at data from various sources to derive valuable insights and describes what happened over a specific duration of time. For example, how many properties were sold in a specific catchment area over a defined period of time.
Diagnostic Analytics
Diagnostic data examines historical data to explain why something happened by searching available data to find valuable insights. For example, why rental properties might have a high turnover rate.
Predictive Analytics
Predictive real estate data analytics predicts what might occur in the near future using diagnostic and descriptive analytics information.
Prescriptive Analytics
This analysis aims to prescribe what needs to be done to take advantage of an opportunity or helps figure out what needs to be done to boost occupancy rates, sales, or sign-ups using modern technologies.
The Process Of Data Analysis
1. The first step is to determine the data requirements or how the data is grouped. For example, if the data is about a customer, will the data be separated by age, income, gender, etc.
2. The second step is collecting data through various sources such as computers, online behavioural insights, environmental sources, surveys, or staff personnel.
3. The third step is the organisation of data on a spreadsheet or other form of software ready for it to be analysed.
4. The data is then checked to ensure there is no error and cleaned up before any analysis is executed.
5 Ways To Apply Data Science To Property Sector
● Property Price Indices
● Automated Valuation Models
● Time Series Forecasting
● Cluster Analysis
● Geographic Information Systems (GIS)
Examples Of How Data Analytics is Being Implemented in the Property Industry
- Enhancing The House Hunting Experience Optimises Consumer Search.
Most homebuyers begin their property search online and don't reach a real estate agent, so agents use consumer data to understand the buyer's journey using data analytics tools. Investors easily find the ideal investment property using data analytics and the most appropriate locations using heatmap analysis based on metrics such as property value and occupancy rate. With the help of data analytics, real estate agents can match customers with homes most likely to appeal to them. Using their preferences like the number of bedrooms, type of neighbourhood, local schools, affordability, and many other data points to enlighten agents and buyers with information that empowers them to make an informed decision.
- Risk Minimisation
The real estate industry is loaded with many investment risks, so data analytics opens multiple opportunities for real estate investors to minimise that risk. A property-Information company like CoreLogic provides valuable tools to measure market trends and estimated forecasts of property values for a period of 1 to 10 years. Property investors extract data and cross-analysis it, this can help them to make crucial decisions.
- The Internet Of Things (IoT)
Agents can obtain location-specific information about properties with the increased access to self-service analytic platforms and a massive drop in the cost of IoT tools in recent years has allowed far more companies access to such platforms.
- Embrace of New Trends In Artificial Intelligence
Artificial Intelligence has seen a massive rise in prominence in property sector business transactions and communications. The Property sector can use artificial intelligence to deliver helpful information about real estate transactions, market value, popular housing trends, and consumer trends much quicker than was ever thought possible, eventually leading to most human interaction with data becoming superfluous.
- Calculating The Value Of Home Renovations
Predictive data analytics can identify what properties local buyers are willing to pay for by analysing the upgrades and improvements of local homes. Agents can recommend specific home upgrades using this valuable information, and they can give this information to sellers to manage their budget.
- Dealers Understand Customer’s Needs Better
Predictive data analytics provided by big data helps real estate agents better understand what their customers want and helps them respond with personalised offers based on the data gleaned.
- Planning New Development Sites
Data analytics can help in identifying the patterns of other successful local developments, allowing investors the ability to analyse in-demand property features locally or nationally to build more desirable housing based on commercial trends.
The Effect Data Analytics Have Had From A Real Estate Perspective.
● Optimising investments
● Unlocking unique insights
● Embracing automation
● Leveraging non-traditional data types
The Benefits Of Big Data In the Property Sector
● Fast And Simple Evaluations
Dealers use property evaluations to set the price of their properties, and investors use them to put forward proposals. Financial institutions depend on data analytics to estimate loans and calculate risk as well as interest rates on mortgages.
● Improvement In Marketing Strategy
Investors and agents are now able to keep their 'finger on the pulse' of the latest consumer and housing trends allowing them to regularly update their marketing strategies based on said information.
● A New Level Of Insurance Services
Insurance companies that provide home insurance use big data to analyse optimal insurance offerings for customers and geographic regions, having up-to-date data on local property value, crime rates, build quality, etc. can allow them less risk when offering insurance to homeowners.
Final Words
The buyers and sellers in the housing market need real estate data analytics to make ideal investment choices because real estate markets are constantly changing. With data analysis, investors can determine the accurate value of properties and where and when to invest. Metrics such as desirability analytics, market cycle risks, and price forecasts are crucial in data analytics.
We are a UK-based business and use database intelligence to analyse addresses and households to find potential house buyers, house sellers, property investors, and the most accurate and up-to-date information about every home in the UK. We give your business decisive marketing information by helping you to identify your future customers and creating measurable campaigns. Real estate data analytics allows people to make decisions about the most profitable investments with as much information as possible.
