Big data in real estate. How analytics services are transforming the market

The continually increasing production and collection of data has led to the term ‘big data’ becoming part of our everyday language. What does it mean, though? In a nutshell, ‘big data’ refers to a large set of data or entire data complexes which is/are too large and complicated for traditional data-processing application software to handle.

How, then, do we define the term in respect of real estate? Well, if big data in general refers to the vast and varied datasets generated and collected in any and every context, we can say that big data in the context of real estate relates to:

• property transactions;
• market trends;
• consumer behaviour;
• economic indicators;
• environmental factors.

All of these indicators constitute a basis for optimising a real estate organisation’s operations on the market.

At this point, it is worth recalling how things worked before the total digitalisation of real estate processes. In the past, transactions relied heavily on manual processes, local market knowledge and personal networks. In order to make strategic decisions, agents and investors would have to sift through piles of data as they tried to make sense of complex local market trends and dynamics in order to take strategic decisions.

The introduction of big data brought significant change in its wake. It was an evolution which granted real estate professionals a wealth of readily available information. Everything from property values to market trends and demographic data could be accessed and analysed with unprecedented ease. The transition empowered stakeholders in the real estate industry, providing valuable insights and enhancing the efficiency and accuracy of their decision making.

Big data analytics goes far beyond collecting the data. It also encompasses turning the data into action, particularly as regards property valuation, identifying investment opportunities and optimising marketing operations.

Property valuation is one of the aspects of the real estate industry that has been revolutionised by big data. Access to vast quantities of data has enabled a much more holistic approach, which draws on and incorporates a wide range of factors, such as neighbourhood dynamics, economic indicators and social trends. This not only facilitates more accurate valuations, but also makes it easier to determine return on investment (ROI), to the benefit of buyers, sellers, agents and investors alike. 

Another key advantage of big data analytics is the capability of identifying subtle but key differences between neighbourhoods. For example, in data on districts where there has been growth, data analysis tools can pinpoint small differences more rapidly and  effectively. They can also analyse an area for untapped potential while simultaneously flagging investment opportunities.

With competition on the real estate market continually growing, effective marketing is an absolute must. Big data can help to improve marketing strategies by offering valuable insights into real estate data, market trends and customer behaviour.

Analysing these data points enables real estate companies to:

• identify potential buyers;
• be more creative about personalising client communication;
• analyse pricing data on the basis of the latest sales and consumer demand data.

This data-driven approach to marketing saves time and resources and that, in turn, is a key to increasing the likelihood of successful deals.

Analysis tools which enable real estate professionals to collect, capitalise on, and analyse information about the market have been mentioned several times in this article. This section provides outlines of those tools.

GISs are now established as a transformative force in the real estate industry. They integrate land data and maps, providing real estate professionals with a competitive advantage through improved visualisation, analysis and mapping of real estate markets.

In property valuation, for example, a GIS can be used to create visual representations, evaluate property values and enhance property value appraisals, not to mention offering access to up-to-date property attributes and local trends. All this information makes it possible to build a more comprehensive understanding of property prices and values, which will ultimately facilitate better informed investment decisions.

Predictive analytics is another tool which is reshaping the industry. It analyses historical data and identifies patterns, empowering real estate professionals to make data-driven predictions about future market movements. At MakoLab we provide data services - check out our offer.

Be it forecasting market trends, assessing property values or guiding investment choices, predictive analytics provide invaluable insights. This information supports professionals in the sector, enabling them to:

• make well-informed decisions;
• forecast future trends;
• identify potential risks and opportunities;
• optimise pricing strategies;
• improve customer satisfaction;
• increase profitability.

These benefits lead to more lucrative investment decisions and forge a competitive edge on the real estate market.

Although artificial intelligence (AI) and machine learning (ML) technologies are still new and not yet fully developed, they can be extremely useful in formulating statistics, automating decision-making processes and providing nuanced insights into market dynamics. One thing is inarguable; these new technologies are going to redefine the real estate market.

The integration of AI, ML and the market has the potential of contributing to the personalisation of the customer experience, a key element in sales today. Personalising property recommendations, saving customers’ time and providing them with a more satisfying experience are all features which can be improved by harnessing AI. The new technologies can also analyse historical data, users’ behaviour and their preferences in order to offer highly targeted listings, revolutionising the way real estate transactions are conducted.

Grasping diverse consumer preferences and behaviours is essential to successful real estate market segmentation. Big data analytics enable professionals to categorise potential buyers or tenants on the basis of a range of relevant factors, including demographics and preferences. This categorisation is a valuable tool for creating effective social media and email campaigns.

Segmentation is the key to increasing the number of successful transactions and makes it easier to run marketing activities. Understanding the needs and preferences of various market segments and adapting their offers to suit specific customer experiences (CXs) allows real estate professionals to adjust their marketing strategies in order to reach the right recipients.

The potential of big data in the real estate sector is vast. Nonetheless, its implementation comes with a number of challenges. This section of the article looks at some of them.

Maintaining data quality and accuracy is fundamental to obtaining reliable insights and decision-making in the real estate industry. Inconsistency of actions, inaccurate analysis, incorrect initial data and other bad practices may well lead to the loss of investment opportunities and customers.

To ensure high data quality, real estate companies can validate geocoding accuracy. When geocoding, however, remembering to ensure the high quality of the input data is crucial, as is entering specific data about the spatial reference system. Equally as vital is bearing in mind that geocoding algorithms may differ from one another.

Implementing big data in real estate involves the multifaceted challenge of integrating data from various sources. The numerous aspects of this include:

• diverse data sources and integration complexities;
• multiple listing service (MLS) systems;
• a lack of standardised data;
• outdated technology;
• inconsistent format and structure.

These factors make integrating data a daunting task. With the right tools and strategies, though, the challenges can be overcome. To achieve this, real estate organisations should:

• identify the data sources;
• understand the characteristics of the data and sources;
• define the data model, taking into account data standardisation in terms of format and usage policies;
• use property data integration tools and application programming interfaces (APIs) to facilitate the integration process.

Data privacy and security are significant concerns when it comes to implementing big data in the real estate sector. Significant volumes of sensitive data are involved in real estate transactions, so protecting them from cybercriminals and ensuring compliance with data protection regulations is critical.

To address these challenges, real estate stakeholders need to introduce robust measures for protecting sensitive information. This includes establishing clear data management practices, adhering to data storage regulations and developing a rigorous privacy policy.

The ethical use of data is a crucial factor in the implementation of big data in real estate. Stakeholders face navigating the fine line between leveraging data for informed decision-making and respecting individual privacy rights.

Transparent data usage policies and ethical guidelines are essential to maintaining trust within the industry, which can ensure ethical usage by:

• respecting individual privacy and dignity;
• safeguarding data privacy;
• ensuring fairness in data analysis;
• upholding transparency and accountability in the use of data.

Given how data-driven the modern world is, it is evident that big data will continue to play a key role in the real estate industry. We can expect the market to expand its operations, going deeper with AI and ML and including emergent technologies such as virtual and augmented reality (VR, AR), smart homes, predictive analytics, energy efficiency, smart city integration, IoT and blockchain technology. Incorporating these technologies will create a real estate market which is better prepared for the changing dynamics of urban life.

Big data analytics can also equip real estate platforms to build personalised property recommendations by analysing behaviour, preferences and trends. This will save customers time and provide a better CX.

Big data is reshaping the landscape of the real estate industry, revolutionising it in ways which range from improving property valuation and identifying investment opportunities to enhancing marketing efforts and implementing data-driven strategies. While the journey towards its full-scale adoption in the sector brings challenges in its wake, the potential benefits are immense.

Looking to the future, we can anticipate an increase in the adoption of data-driven strategies and the integration of emerging technologies, along with a heightened focus on personalisation and customisation. One thing is for certain; with big data on board, the real estate industry will never be the same again.

How is data used in the real estate industry?

Real estate professionals use data to analyse market trends and make informed decisions about investments, negotiations and special offers. Data also enable them to obtain actionable insights relating to sales, purchases, rentals and property management, while leveraging real estate data analyses equips them in terms of anticipating financial viability and optimising their strategies in order to maximise success.

What are the five P’s of big data?

The five P’s of big data are purpose, people, processes, platforms and programmability. Each of these elements is essential to the effective management of data science projects and extraction of knowledge from data.

What impact do big data analytics have on personalisation on real estate platforms?

On real estate platforms, big data analytics facilitate personalised property recommendations, enhancing the customer experience with customised options based on individual preferences.

What are the key obstacles connected with data privacy and security as big data is being implemented in the real estate sector?

The key obstacles connected with data privacy and security while big data is being implemented in the real estate sector include concerns about data security and privacy. Clear data management practices, adherence to data storage regulations and the development of a robust privacy policy are essential to overcoming those challenges.

What are the applications of GIS in property valuation?

GIS is commonly used in property valuation for visual representations, the evaluation of property values, accessing up-to-date property attributes and local area trends and enhancing property value appraisals.

1. Andrew Beattie, ROI: Return on Investment Meaning and Calculation Formulas, Investopedia
2. Big Data Framework, A Short History of Big Data, Enterprise Big Data Framework
3. ESRI, What is GIS? A spatial system that creates, manages, analyzes, and maps all types of data
4. Forbes Technology Council, Ensuring Data Privacy: 20 Best Practices For Businesses In The AI Age
5. LinkedIn Real Estate, How do you use AI or machine learning in real estate?
6. LocationIQ, Geocoding accuracy
7. Sherry Tiao, What Is Big Data?, Oracle

Translated from the Polish by Cary Swift