How Data Analysis can help your Real Estate Investment?

Data analytics is revolutionizing the real estate industry.


Nowadays, data analysis play a big role in making decisions and helping a business operate more effectively.

Data analytics involves compiling data statistics for industry predictions, decisions, and motivations. 

Data collected may be from consumer and business surveys, government or public databases, census figures, or collecting information online. 

Let’s discuss what’s this buzzword about and how property management can benefit from data analysis. 

What is data analysis?

Data Analysis is a process of inspecting, transforming and modeling data with the goal of discovering useful information, informing conclusions and supporting decision-making. 

Business intelligence covers data analysis that relies heavily on aggregations, focusing mainly on business information. 

In statistical applications, data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA).

Varieties of data analysis

EDA focuses on discovering new features in the data.

CDA focuses on confirming or falsifying existing hypotheses.

Predictive analytics focuses on the application of statistical models for predictive forecasting or classification.

Text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. 

Unstructured Data

Unstructured information is information that either does not have a pre-defined data model or is not organized in a pre-defined manner.

It typically contains data such as dates, numbers, and facts.

Business Intelligence

Comprise the strategies and technologies used by enterprises for the data analysis of business information. 

It provides historical, current and predictive views of business operations.

It can be used by enterprises to support a wide range of business decisions ranging from operational to strategic.

BI technologies handle large amounts of structured and sometimes unstructured data to help identify, develop, and otherwise create new strategic business opportunities.

What are the common functions of BI?

  • Reporting
  • Online analytical processing
  • Analytics
  • Data mining
  • Process mining
  • Complex event processing
  • Business performance management
  • Benchmarking
  • Text mining
  • Predictive analytics
  • Prescriptive analytics

Here are some ways to use data analysis in real estate and its importance:

  • Pricing 

Price optimization is the key to your business success and obtaining the in-depth property value information statistics can provide knowledge effective decisions on pricing.

Implementation of data analytics can ensure effective pricing knowledge.

Pricing data analytics is not just for buying and selling prices, but as well as can be applied to all aspects of real estate, from the pricing of services, pricing of taxes and rates, building prices, rentals, and statistics.

Agents need accurate pricing data to advise owners and investors on the best pricing decisions.

  • Location

Data Analytics and Big Data can create a multitude of variables in creating complex locations predictions and assessments.

Data analytics provide critical insights into the latest location trends.

Data analytics on location can contribute to a wider view of the location, including crime rates, school ratings, traffic congestions, and quality of life.

  • Client Demographic

With data in consumers, everyone can make informed decisions.

Using this data developer can determine what type of project to be considered. Agents can more effectively target the right types of client for sales. Homeowners can determine what renovations may or may not be wise. 

What type of people is investing? Are people mainly buying or renting? What type of houses are people looking for?   

  • Lending Demographics

What is the trend on interest rates?

Knowing what banks or financial service company are providing can give a customer a step up when it comes to applying for lending.

Banks and other financial service company can benefit from real-estate data analytics when deciding whether to lend to a client, data analytics can be used to create more effective fact-based lending decisions.

Data analytics can also be used for analyzing sales prices on foreclosure.

  • Commercial Trends

A commercial venture requires in-depth motivation based on the current consumer market.

Analyzing data on consumer trends can provide accurate and more insightful information about the current market.

What type of businesses exists in the industry? Are there enough shoppers for a new mall? Are diners looking for a new restaurant? Is there a shortage of office spaces in the financial district for the development of a new building? Answering these questions can help with planning and pitching of commercial projects.

  • Quicker and more accurate appraisals

All appraisals are driven by the current market rates for sales.

Data analytics provides huge volumes of accurate information on sales for similar properties.

This will ensure valuators can provide price estimations far more quickly and accurately to interested parties. Data analytics will reduce the variation between valuations and makes them easier to obtain.

An appraisal is important and must be accurate especially for lending, buying, investing, and insuring a home or property.

  • Town Planning

Population and usage statistics are readily available in government.

Combining consumer trends, population demographics, and property data can show which policies are best suited to the community.

This may be to determine zoning needs, drive decisions on types of permits to approve, or solve debates on the use of public areas. 

Data analytics can help town planners and communities resolve real estate planning. It can be used to effectively create fact-driven town planning strategies that better meet the needs of users.

  • Targeted Advertising

Data analytics can be used to determine how best to target paid and organic traffic.

Consumer data can be used to target advertising to the market where it is more effective.

Advertising data can be used to drive sales campaigns.

Targeted advertising can be used to reach lenders.

Data analytics can make advertising more likely to succeed.

  • Competition Analytics

Competition analysis through data improves performance and user experience.

Consumer information on real estate services can also help illuminates scams and ensure users find trustworthy and reputable agents.

Data analytics can provide performance reviews on different types of real estate businesses or agents. For example who is selling the most? What consumer rating a business receives? Which developers are the most trustworthy? Which real estate businesses to invest in?

  • Additional Revenue Channels

Compiling data for resale is a huge industry in today’s real estate industry.

Compiling new methods of presenting real estate data analysis for consumers may provide opportunities for new online businesses in the real estate market.

For data-driven revenue, real estate professionals may be a compiling comparison sales reports to help gain new clients, compiling demographic data such as employment types and rates, schools, and quality of life, or trends in population which affect real estate sales.

What are the potentials in real estate analytics?

  • It helps customers and real estate agents quickly find an appropriate building with indicated parameters.
  • It makes possible for costumers to appraise how profitable the purchase of a house or apartment will be for them.
  • A huge amount of data can now be analyzed and processed much faster.
  • It makes the workflow much faster and more efficient.
  • It allows real estate agents to find a good location for building a new house from scratch.

Data Analytics Advantages in Real Estate

  • Risk reduction
  • Improved customer’s engagement
  • Accurate appraisals
  • AI-enabled decision-making process
  • Enhanced marketing strategy
  • Data-driven insurance

How data analytics can drive better real estate investing?

Improving the real estate investing experience is designed to enable sellers to gain unprecedented degrees of real-time-data-down to the property level via a dashboard, which helps them make more informed decisions.

When investors can quickly evaluate an asset and minimize any risk that can come after the purchase, they appreciate working with the marketplace that provides this kind of level.

Data analytics drives better execution on every property sold, attracting more qualified buyers. With data more readily available, investors can easily decide with confidence.

Data promotes transparency and builds a foundation of trust.  Effective use of data can create a better environment for buyer and seller where they can interact directly, making more informed and confident decisions while mitigating unwelcomed surprises later in the process.

Therefore, access to high-quality data not only enhances the real estate experience but is also a key to keep the marketplace thriving.

Applications of Big Data in Real Estate analysis

The main purpose of using big data in real estate is to analyze large datasets from multiple sources and extract useful information from them. 

It is also used to analyze property values. 

Re-shaping Property investment through Data Analytics

Improved availability of quality data analytics in the real estate industry is fast providing real estate tools to reduce stability and enhance growth.

Using a data-driven approach in real estate is essential in today’s market.

With data analytics in real estate, private and commercial investors alike can make more informed decisions which can minimize their risk.

Transforming Real Estate investments strategies through Big Data

Big data is changing the way real estate operates and make business. The possibilities of collecting, analyzing, and extracting conclusions from big data sets have an impact in almost all industries. 

One of the most recent real estate technology trends is using big data software in advertising and marketing.

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