Data is one of the most important assets for any company. You could have the best resources, software, and properties. If you forego data analysis, you’re still leaving your company’s success depending on pure luck.
You can follow your instinct for many of your decisions. That said, any market and industry includes countless factors—i.e. Competitors, consumers, economics—each one with its set of variables.
That’s why data science, particularly data analysis, is vital for any company. It provides the right groundwork to nurture informed decisions. That means reliability, boosting your success chances noticeably.
What does that mean for the real estate industry? How are people using data science? What can you do to add this skill to your roster? That’s what we’ll find out today.
What exactly is data analysis?
Data in business is the basis for several approaches and techniques. People tend to use data analysis and science interchangeably, and the same is true for big data. However, we must note that they’re different terms. SimpliLearn has a great article explaining the differences.
Essentially, data science is the main term. It encompasses several techniques and skill sets, including data research, preparation, and even analysis. That means that data analysis is effectively part of data science.
Technically, it’s not wrong to use data analytics and data science interchangeably for the real estate industry. After all, data analytics is the most prominent data-science-related skill used in the sector.
On the other hand, big data is somewhat separated as a concept. It refers to considerable amounts of data. Big data can be part of data science, but it’s not an interchangeable term. It just describes large data volumes, and not all companies employ big data. It requires more resources in exchange for much more information access and leverage.
Why is data analysis important in all industries?
Data analysis tends to have different advantages depending on the industry. Still, virtually all companies, in all sectors, can leverage data analytics to grow. The most common use for data analysis across the world is to influence decision making.
That’s because making choices, regardless of your sector, is a lot more effective if facts are the foundation. Data science can influence other industries in terms of process automation and management.
That said, we’ll focus on the general benefits here.
Targeting
Data from your customers and marketing campaigns shed light into ideal advertising methods. You can show people what they need in the way they like it, thus boosting sales noticeably.
Additionally, data analysis allows you to figure out how much you’ll have to invest in marketing and your returns on investment. It’s great for optimizing your budget and finding where you could improve your marketing.
Customer data
Speaking of customer data, that’s a benefit in its own right. You’ll need this information for several of your strategies and goals: better marketing, acquiring the right assets, ideal audiences, and more.
In the end, all businesses come down to selling, and real estate is the same, fundamentally. You need clients if you wish to turn a profit, and you need to learn who these clients are. Customer data enables you to do so.
Innovation
Understanding and taking advantage of market trends boils down to data analysis. Things like sales, advertising expenses, and market performance are purely data. Companies studying this data can find patterns they can leverage.
Realizing new trends before your competitors enables you to adapt quickly and offer what’s starting to become popular. The competitive edge you gain from innovation is one of the best ways to achieve your goals.
Cost-effectiveness
Data analysis isn’t solely about selling, though. Data analysts can be a big advantage if you wish to lower and optimize your costs. Data points you towards departments and processes that might be receiving a larger budget than they need.
The same is true for marketing expenses. You can assess which marketing strategies aren’t yielding the results you expect. Therefore, relocating capital from these methods to ones that work reduce your overall budget while increasing profits.
Problem solving
Problems are common in all businesses throughout all industries. They’re not always catastrophic, but that doesn’t mean you shouldn’t solve them quickly. All problems cause halts in your operations, as little as they might be.
Data analytics spot where these problems might be coming from. The same is true for finding out what you can do to solve and prevent them in the future. Consequential problems are also easy to spot and shut down before they occur.
Data accuracy
Data analysis doesn’t simply assess the data you’re getting. It also ensures said data offers quality, actionable insight into the market and your business. This advantage is vital for company growth, as it lets you spot what you’re doing wrong.
The same is true for forecasting and formulating strategies. You can use market data to find out which methods have been working within—and outside—your industry. Data science is vital for both studying information and ensuring its quality.
Marketing anchor
Data analysis is so advantageous for marketing because it gives you the foundation for your strategy. In the end, your customers’ likes, needs, and preferred marketing methods are data. Therefore, studying this data saves time finding out what’s working.
It’s particularly important when we consider the insights from market data into customer behavior. If you’re using social media marketing, you can gain insight into what’s working and what isn’t, even within your advert’s design.
Forecasting for the new year is vital
Forecasting is critical for countless businesses. We’ve seen companies forego this technique because of a single misconception. People usually see forecasting as prediction, as a “what if” idea.
That leads them to believe forecasting is trivial, thus avoiding all its advantages. In reality, forecasting can be an active element working towards your goals. It allows you to see where your company is going right now, thus allowing for revising strategies and adapting to pressing problems.
In this Forbes article, the main topic is forecasting for 2021. After the huge obstacle presented by 2020, we can agree that preparing for a new year is vital. That’s not the point, though.
If you read the article, you’ll spot what they point out as the fundamentals:
Industry growth.
Understanding customers.
Resource planning according to demand.
Sales opportunities.
All of these are part of data analytics. As we mentioned, data analytics lets you spot trends and identify your target audience. Therefore, data science in general makes up the backbone of forecasting, and thus, it’s critical for setting and revising goals and expectations.
How are real estate firms applying data science?
The real estate industry depends on big data to a large scale. Every property—both commercial and residential—has plenty of variables and information. You need to track lease agreements, property value, maintenance expenses, renovations, and more.
Then, each lease agreement has lots of information by itself: terms, clauses, responsibilities, and compensations. That means your company likely has countless data sources and variables to account for. That’s not even mentioning market information.
That’s why data science is more than an advantage for real estate firms. Knowing how to leverage the amounts of information available in your industry is vital to ensure you achieve your goals and stay ahead of the competition.
Pricing indices
Every real estate transaction is unique. Even two apartments in the same building aren’t identical, and that affects property pricing, conditions, costs, and more. That’s one of real estate’s main challenges since it makes market performance harder to assess.
Transaction averages are tricky because of property type variations throughout the market. Data science has bred methods to curb this problem, including specific techniques and filters for the specific information you want to look for.
Valuation automation
Valuation is gaining lots of traction when it comes to statistical approaches. Automating valuation aims to come up with market value estimates for different properties. The immediate benefit is that property market value is usually more accurate.
However, it’s not simply more accurate. It’s also cheaper and faster.
Time series forecast
Time series are useful for figuring out upcoming trends. Forecasting allows you to create a strategy towards specific goals and milestones, thus improving your investment and efficiency. Data analysis is perfect to turn existing data into accurate forecasts.
Thankfully, the real estate sector has plenty of information available. You can use property prices, markets, GDP, employment rates, inflation, and more. Additionally, mortgages and loan interest rates are real-estate-specific metrics that can provide invaluable data.
Cluster analysis
Real estate is quite unique in that performance varies significantly throughout different locations. Even cities within the same state can be completely different. The same is true for neighborhood and sectors within said cities.
Cluster analyses allow companies to spot data patterns for specific property groups, which tend to perform similarly. This data science method lets us measure which markets perform similarly, and use these as reference for performance measurement.
GIS
Geographic information systems are huge advantages for real estate analysis, particularly for visualization of local areas. With governments becoming more open with their data sources, data analysts have a plethora of sources to choose from.
You can use GIS to establish a property’s radius. That’s useful for evaluating properties near commodities, like train stations, malls, and other facilities that could boost their value. The same is true for figuring out commuting and property criteria.
Data-driven decisions: are they better?
According to the Harvard Business School, using data to back your decisions is a huge advantage. People tend to overestimate the value of “gut instinct” as a virtue instead of a disadvantage.
Sure, following your intuition can be fine in minor decisions. However, managing a business turns every decision into profit or money loss, thus rendering intuition as a liability in most cases.
That’s not to say that intuition is always bad. It’s still a great starting point to start your research and spot possible opportunities.
How to become data-driven
The same Harvard Business School article has several tips for companies planning to become data-driven. If you wish to give more priority to data in decision-making, you can take 3 fundamental steps to get there.
Pattern searching
Data analysis is, essentially, studying market information to figure out exploitable patterns. The first step to implement data analysis into your company is to study data consciously. With enough practice, you’ll start approaching data sources with an analytical approach by instinct.
Decision tying
Again “gut instinct” is our primary driver when it comes to making choices. That’s natural, and it’s not detrimental unless we rely purely on our intuition. Make sure to look for data support for every choice you plan to make.
Education
Finally, training your resources or hiring data analysts is always an advantage. Having a specialized staff for studying data will ensure many of these steps are already taken by them.