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Real Estate Web Data has always been a blend of intuition and common sense. Both buyers and investors are always on the lookout for the best property off which to make the most profit. But being slow in your approach would mean losing out to your competitors, which is where web scraping real estate data comes in. Most people who have taken a course on machine learning or neural networks would remember the Boston Housing problem as one of the first problems that they had solved. In this particular problem, people are provided with a data set of houses with different features and attributes. In real life too, people have started to use data (which they usually crawl from real-estate listing websites) to predict prices of houses when they are buying or selling, or renting a property. Many real-estate websites have integrated these algorithms and are providing these estimations as paid services.
An estimation of the property prices in an area will help both the buyer and the seller. For example, if you were to know that 3BHK flats in an area go for around $100,000 then you could set your expectations accordingly. Then again, two similar flats in the same apartment complex can also have varied pricings- furnishings, bigger balconies, feng shui compatibility (it is a pretty important deal for those who believe in it), all these and many more factors become important data when setting a price for a property.
While these are more accessible features, others such as a swimming pool, a terrace garden, even a penthouse suite scream luxury and increase the property value manifold to cater to the coveters of such a lifestyle. Thus, it is clear that the features of the property help in setting down a price. However to get the best estimations you will need data on different types of properties with different types of features. The more data you have, the better your model will be, and the better your model is, the closer will your predicted prices be to reality. This process can be used by both people who are buying or selling real estate to make sure that they are not duped and they can make the most of their investment.
While the price of a property is governed by its features based only on said features. The optimum variables required by a tenant are different from someone meaning to stay in the property. For example, a swimming pool, though a bonus, is not something a tenant would be particularly after. The location from college or workplace, nearby markets, and quiet neighborhoods- these are features that will excite him more. The rent of nearby areas would be helpful for you to set a value on your property. In most cases, rental properties’ price factor depends mostly on the amount of furnishings present. Therefore, if you are an owner looking to rent out your property, these are data you should be focusing on. And if you are a seller, quoting this data would give you an edge in the real estate market.
A higher vacancy rate means an undesirable neighborhood while a lower vacancy rate indicates a desirable one. Vacancies are not good, and you will not want to invest in a place where most buildings have loads of vacant flats. Many factors could lead to a higher vacancy rate- renovation, higher than usual rent, a long period between a renter moving out and the new one moving in. As an investor, it becomes important for you to consider the vacancy rate as the higher the number of vacant flats, the lower the market value of the property. Analysis of data related to vacancy rates in different parts of a city will allow you to make better decisions when you are looking into the real-estate business.
While you could depend upon your intuitive skills, having real data at hand will always let you make a profit. For example, after the 2008 financial crisis, the housing market fell apart. In a way, that was the best time to buy a property since it would have been the cheapest then. Such market extremities could only be taken advantage of when you have current as well as historical data.
If you are a buyer, you must dig into the data related to a place before buying it. While being near schools, colleges, restaurants, hospitals, and more, lead to higher value and sale-ability of the property. However, it is not as easy as it sounds to predict the success of a property. For example, Seattle saw an increased interest in apartment buildings within a mile of stores like Whole Foods. But while having more stores in proximity should mean a jump in value, the opposite was true. This goes on to show that prices do not correlate with quantity but the quality of community features.
The cost of nearby apartments also influences the value of the property. An apartment in an upscale neighborhood like SoHo would mean greater profit than one downtown.
It becomes apparent that while much of real estate depends on how confident you are. A data-driven approach would not just help you in buying or selling a property, but also to predict the market. In Seattle, both traditional and non-traditional data were used to build an application that correctly predicted the three-year rent per square foot for buildings. While two buildings may seem similar to the eye, a data-backed evaluation could lead to different outcomes. If you are looking for similar data you should reach a web scraper service provider like PromptCloud
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