The real estate industry has always lagged behind when it comes to technology, with outdated systems, manual documentation reigning supreme. However, things are changing, thanks to innovation and customer behavioral upheavals following the COVID crisis. Many sites like the Real Estate Provider, are able to give you with precision the real estate value of an address, then, taking into account an ultra-powerful algorithm, to provide you with the real estate prices of a property. .
Can technological advances in the real estate industry really help predict real estate prices?
Use algorithms to predict real estate prices
Savvy buyers and investors are interested in property value rather than price. Just as the price of a stock gives no indication of its intrinsic value, and you have to look at the debt ratio and other ratios to estimate the value of the stock, the price of a property Real estate says little about its real value, and you have to look at the underlying data to know the real value of a property.
The opacity and lack of access to the underlying information about the property has hitherto prevented a correct and objective analysis of the real value of the property. Often the only information available to stakeholders has been the prices at which previous properties of a similar nature in the same locality have been sold. The asking price for properties has always been subjective, based on what the seller thinks the property is worth, with a vague estimate of the prices at which other properties have been sold in the locality and other subjective criteria forming the basis. For the asking price.
How Does Technology Predict Real Estate Prices
However, things are changing and evolving quickly. Real estate data is increasingly voluminous. Many companies have already deployed several new tools and services that leverage this data. Others are in preparation.
The Real Estate Provider estimates that around 90% of buyers will search for real estate information online as a first step in their buying process. Most of them take into account crime rates in the neighborhood, the availability of public transportation, the profile of local businesses such as gyms, grocery stores, restaurants, and several other factors.
The situation is improving. Machine learning, in addition to promising a scientific basis in real estate pricing, now instills objectivity and transparency.
Machine learning models, made up of hundreds of variables, provide insight into almost every aspect of a property, with a very high level of precision and objectivity. These models sift through stacks of data and are able to not only pinpoint an accurate price for the property, but also identify hidden gems among the many properties available for sale. Buyers can use this information to set a fair and accurate price for the property, and sellers can also set a fair price and sell their property faster, without the risk of being undervalued. Banks and financial institutions can use this information to offer loans based on the expected future value.
Identification of correlated variables
Buyers and investors often end up making unscientific comparisons between two properties and are often deceived by a lower price for a property, oblivious to the much lower value on offer.
While obvious variables such as number of bedrooms and square footage may be obvious and self-explanatory, some variables are less so.
Some variables are also strongly correlated with others. Some pairs, correlated by nature, such as “Finished area of basement” and “Unfinished area of basement”, and other pairs, correlated by inference, such as “General condition” and “Year of construction”, help identify the real value of a property in relation to the asking price.
Machine learning models take these variables into account, but also give appropriate weight to each of them. For example, two houses may look similar while taking into account the obvious variables, but Property A may offer a much better value due to the superior quality of the plumbing materials used in construction and the availability of groundwater compared to property B.
Uncover seasonal factors
Seasonality has an impact on property prices, but this impact can be subtle or hidden. Using historical data, we obtain explicit trends on the selling prices associated with the seasons. For example, sales may be higher during the summer months when the new school year begins, causing prices to skyrocket. These seasonal prices help real estate investors get the best value from their purchases, sellers to value their property more accurately, or delay their sale for a few months to get a better price, and more.
Identify the true value of external factors
Buyers always consider the neighbourhood of the property, in terms of crime rates, the quality of nearby grocery stores, proximity to schools, and other factors. However, this analysis is often performed on an ad hoc basis and on a generic basis. The “devil” in detail can often be overlooked. Machine learning models take into account external factors at a much deeper level, correlated factors such as frequency of power outages in the locality, unemployment in the locality, frequency of transport links, rating of schools and many more, which not only provide an objective and scientific basis on the real value of the property, but also have an immediate impact on real estate prices.
The real value of machine learning, however, is the ability to pull trends and valuable insights from data. For example, analysis of police arrests and chemicals in sewers indicates crack consumption, which suggests gentrification in the near future. When crack is replaced with cocaine, it may indicate that gentrification may already be over. Such information may not be available for everyone to see and may be impossible to decipher without analysis of this underlying data.
While real estate technology today makes it easy to predict real estate prices with a high level of accuracy and objectivity, the challenge is to build models of the underlying data in a robust way. The quality of the machine learning system depends on the quality of the algorithm that powers it. Success depends on creating linear models, co-opting all possible categorical variables, and historical data associated with each variable.
