With housing prices exploding all over the country, it's getting harder and harder to find a good deal on property. Mashvisor pools up-to-date data from resources like the MLS, Zillow, Rentometer, Airbnb, and the US Census Bureau to give you the most accurate, current real estate market data available in a single place. All data is stored and analyzed using AI and machine learning, turning raw data into actionable analytics for you and your business. Mashvisor offers comprehensive coverage for 95% of US markets, both large and small. All you have to do is search and you can find the best long-term and short-term rental markets in the US, filter based on your desires, and identify high-potential properties for your most important criteria, or to better understand the neighborhood you're looking to buy in.

Real estate markets depend on various methods to predict housing prices, including models that have been trained on datasets of residential or commercial properties. Most studies endeavor to create more accurate machine learning models by utilizing data such as basic property characteristics as well as urban features like distances from amenities and road accessibility. Even though environmental factors like noise pollution can potentially affect prices, the research around this topic is limited. One of the reasons is the lack of data. In this paper, we reconstruct and make publicly available a general purpose noise pollution dataset based on published studies conducted by the Hellenic Ministry of Environment and Energy for the city of Thessaloniki, Greece. Then, we train ensemble machine learning models, like XGBoost, on property data for different areas of Thessaloniki to investigate the way noise influences prices through interpretability evaluation techniques. Our study provides a new noise pollution dataset that not only demonstrates the impact noise has on housing prices, but also indicates that the influence of noise on prices significantly varies among different areas of the same city.

In the housing search space, all eyes are on the two Juggernauts: Zillow and-- realtor.com And most of the discussion is about who has more listings, who has a better UI, etc…. However, there has not been a lot of talk about the innovations that will reshape the industry. Here are some thoughts I would like to share with you and which I would appreciate your thoughts. Personally, I see three potential directions that innovation in real estate tech could go.

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"The ad said this pool was lagoon-like. That's the sarcastic comment of two clients in an iconic scene from American Beauty right before Carolyn, the movie co-protagonist working as a real estate agent, has a meltdown. Despite the typical cinematic overdramatisation, it's actually true that few industries in the world are driven by the "human factor", including interpersonal interactions between sellers and buyers, as much as real estate, and this seems to result in a certain conservatism and low propensity for change. Just a few years ago, indeed, this sector was ranked as the second least digitised by the Morgan Stanley Digitalisation Index. However, things are evolving faster, as relying on technology to compete in a saturated, unpredictable market like real estate can certainly come in handy.

The pricing of housing properties is determined by a variety of factors. However, post-pandemic markets have experienced volatility in the Chicago suburb area, which have affected house prices greatly. In this study, analysis was done on the Naperville/Bolingbrook real estate market to predict property prices based on these housing attributes through machine learning models, and to evaluate the effectiveness of such models in a volatile market space. Gathering data from Redfin, a real estate website, sales data from 2018 up until the summer season of 2022 were collected for research. By analyzing these sales in this range of time, we can also look at the state of the housing market and identify trends in price. For modeling the data, the models used were linear regression, support vector regression, decision tree regression, random forest regression, and XGBoost regression. To analyze results, comparison was made on the MAE, RMSE, and R-squared values for each model. It was found that the XGBoost model performs the best in predicting house prices despite the additional volatility sponsored by post-pandemic conditions. After modeling, Shapley Values (SHAP) were used to evaluate the weights of the variables in constructing models.

PriceHubble is a PropTech company with over 220 employees, set to radically improve the understanding and transparency of real estate markets based on data-supported insights. We aggregate and analyze a wide variety of large scale datasets, and apply state-of-the-art machine learning to generate high-quality valuations and predictive analytics for the real estate market. We are headquartered in Zürich, with offices in Berlin, Hamburg, Paris, Vienna, Prague, Amsterdam and Tokyo. We work on international markets and we are backed by world-class investors. We have a startup environment, low bureaucracy, and an international team and business. You will be part of the Data Products team, heading a team of experienced ML and software engineers who build PriceHubble's Data and ML Platform.

Realiste, a leading AI developer in the real estate market has finally launched in the Middle East, changing the real-estate and housing landscape using the latest advanced technologies within all operations. Realiste is an AI-based real estate market development company committed to providing users with accurate and timely market value appraisals. The company's mission is to digitise the real estate market of every major city across the world, while offering free access to appraisals and personalised recommendations. Since launching in December 2021 it has established operations in Riyadh, Dubai, London, New York, and Moscow and are planning more expansions in the next year. Realiste has successfully fielded an application capable of monitoring the market round-the-clock and displaying the true value of real estate, as well as predicting price movements of properties for the next 6 to 12 months.

Dubai: In the last decade the MENA region has proved to be one of the world's most lucrative fields for the deployment of AI technologies. Realiste, a leading AI developer in the real estate market has finally launched in the Middle East, changing the real-estate and housing landscape using the latest advanced technologies within all operations. Realiste is an AI-based real estate market development company committed to providing users with accurate and timely market value appraisals. The company's mission is to digitize the real estate market of every major city across the world, while offering free access to appraisals and personalized recommendations. Since launching in December 2021 they have established operations in Riyadh, Dubai, London, New York, and Moscow and are planning more expansions in the next year.

Things have definitely changed in our everyday lives as a result of the technology revolution that has gained traction over the last decade. Investing in real estates, like many businesses, is undergoing transformations because of technological advancements. Artificial intelligence is one of the most potent technological advances changing real estate investing today. In today's development marketplace, artificial intelligence is the most major game-changer. That appears to be a tendency that will continue in the next few years.