Understanding the Real Estate Market with Data: Real Estate Analytics & Better Historical Rental Pricing Model
Learn how real estate analytics can improve historical rental pricing models, reveal market trends, and support smarter property pricing decisions.
The business of real estate has always been one of informed speculation.
Investors try to determine which areas are likely to grow, property managers try to get out ahead of changing demand, and even single landlords try to guess what kind of income they can expect from their properties in the years ahead. What is new is the wealth of data now available to help guide these decisions. From rental demand to average prices and location to occupancy, data analytics can reveal both the specifics of a house for rent in Da Nang and a broader picture of individual transactions and trends. Although few would have trouble understanding the big-picture lesson that can be learned from a search for a house for rent in Da Nang, specialists like Atlantis Data Solutions turn the larger real estate market data into actionable insights.
Understanding the Market Is More than Understanding a Single Sale
A property for sale may tell you everything you need to know to make a decision about one particular unit, but it does not tell you the whole story of a given market. An asking price may suggest the goal of a potential seller, without providing much information about the actual sales price people are paying. A similar analysis can be applied to a typical property for rent in Da Nang, Vietnam. Landlords often advertise the price for what they want, but this does not necessarily mean that comparably quality properties are going onto the rental market for the same amount, or that the unit is not going to stand vacant for months on end.
This is where data is particularly critical. Investigating the previous sale prices, rental price adjustments, vacancy rates, and demand habits can place any new property into a larger context. Instead of asking if it is a good deal relative to your budget, you can ask instead if your property is a good deal relative to your competition or even if the larger market will continue to support more growth.
Pricing Models Are More Advanced
It has never been difficult to estimate what a property is roughly worth. Compare the investment to properties nearby. Determine the overall condition of the property. The general location. Size. Compare to any other or every other. But today, a pricing model for a home for rent in Da Nang has data points that extend beyond any one point buy one sale up in the city.
The value of these models isn’t that they hand you an answer; you should always follow whether your model says you can push for a higher rent or that your property might be overvalued, you still need to apply judgment and check to see if the answer seems right based on your experience and other non-model data points. But having a consistent starting point is a huge help.
The same idea applies to pricing rent as well. It isn’t just the cost of ownership plus some markup for whatever the landlord wants to earn. Renters are making a comparison of multiple alternatives. They are responding to changes in supply and demand. They are making a choice about how far they want to live from their work, favourite restaurant, etc. A model can help a potential landlord understand how the property compares to other properties in a given geography.
Neighbourhood Changes Can Be an Informative Signal for Action
More generally, data can help identify when changes are starting to happen before they become obvious based on price. Land prices and, by extension, rent prices don’t change overnight. Instead, characteristics change and, all else being equal, assets with those characteristics become more or less valuable. These signals might include more rental inquiries coming in, retail businesses opening or closing, changes to infrastructure (more or fewer buses for the commute), age or other demographic changes in the local population, changes in the kinds of units being built and where they are.
Rent Is Never As Predictable As You Wish That It Were
Almost everything other than the physical quality of the asset can dictate how much demand there is for housing. The price of oil could, to some extent. Availability of lima beans does, to some extent. Employment patterns, tourism, school schedules, local shopping opportunities, the logistics of infrastructure spending with major future road closures, transient Airbnb properties reducing supply, etc. Economics is always changing. Anything that impacts the liquidity and the economy more generally can impact supply and demand for your home.
Property Management Will Become an Iterative Data Collection Exercise
However, the use of analytics does not stop once an investor owns an asset. Property management itself generates data, from the cost of maintenance to the level of tenant turnover, to the length of vacancy periods, to the rent paid, to recurrent maintenance issues. By analysing this data, a landlord can spot areas of inefficiency in how a property is being managed.
The data with the greatest ability to unlock value is likely to be data on maintenance. Not only can data on recurrent repairs show a landlord where they could use resources most effectively, but also where an asset may be at the point of obsolescence. Many times, the best course of action for a landlord is to replace an old item of repair in an asset rather than consistently fixing something that is breaking.
Real Estate 2.0: A More Adaptive Model
As good as you may get, real estate investing is not likely to become a completely predictable exercise. This isn’t just because the algorithms aren’t good enough, but more to do with the fact that variables can change. Economic conditions are hard to predict, regulatory decisions can have unpredictable consequences and renters are people with free will and taste all their own. Even the patterns of the colour-coded maps can’t predict what’s in store completely. So what are we doing?
The goal is not to eliminate uncertainty; it’s to reduce it and make decisions more adaptive. For investors, that may mean testing the assumptions behind a deal against macro market dynamics rather than the asking price.
Ideally, the most valuable data is the one that causes a user to change their decision. When pricing models, neighbourhood data, renter preferences and on-demand performance data can be accessed in one platform, the market can be more easily viewed as a dynamic entity rather than a collection of buildings. That kind of understanding allows businesses and professionals to adapt and move with the market, but to do so in a way that is grounded in logic rather than wishful thinking.
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