Knock is on a mission to make trading in your house as simple and certain as trading in your car. Started by founding team members of Trulia.com We share the same top-tier investors as iconic brands like Netflix, Tivo, Match, HomeAway and Houzz. We're You will get to work with some of the most interesting data sets and have the freedom and responsibility to help shape our core products from initial data exploration to production models. You will design and implement the foundation of our predictive models and work closely with other team members to test the accuracy in local markets.
Germany's and Europe's leading real-estate service provider Apleona is investing in a self-learning technology based on artificial intelligence (AI) in the field of energy and CO2 optimization. The company, which specializes in integrated facility and real-estate management, has therefore acquired an interest in Bonn-based green tech company Recogizer. Recogizer uses its "energyControl" AI system to create digital twins of buildings and their technical facilities, enabling fully automated predictive control of heating, ventilation and air conditioning technology in real time, all while predicting and taking into account the environmental conditions and usage parameters of each building. This achieves average energy savings of 20% to 30% and a comparatively large reduction in CO2 depending on the energy supply situation. At the same time, this smart system ensures a comfortable and ideal room climate for the user; one that is not affected by short-term changes in weather, the way the building is used or how much time the user spends there.
New York-based Cherre real estate data and analytics platform announced it has raised $16 million in growth funding. Including this round of funding, Cherre has raised a total of $25 million. Intel Capital led the funding round. Navitas Capital, Carthona Capital, Zigg Capital, Dreamit Ventures, and Silicon Valley Bank (new growth debt relationship) also participated in the round. Cherre's artificial intelligence platform empowers large enterprises, insurance companies, banks, and investors with a platform to instantly collect, augment, resolve, and analyze datasets in real-time from hundreds of thousands of public, private, and internal sources.
Artificial intelligence (AI) is quietly infiltrating the real estate industry -- without looking like a futuristic takeover but rather a boon for buyers and sellers. Mortgage lenders, realtors, title companies, property appraisers and consumers use AI for a wide variety of purposes, including application automation, expediting processes, chatbots on real estate sites and automated valuations models, or AVMs, to name a few. "AI can benefit real estate industry participants in many ways. An example is the use of machine learning to link potential buyers with more relevant properties, creating an enhanced real estate transaction (more timely and focused)," says John D'Angelo, managing director at Deloitte Consulting LLP. "This can also make it easier for buyers and sellers to receive more personalized offerings based on their preferences. In addition, AI can reduce the transaction costs for buyers and sellers by shortening the transaction cycle."
While it was once considered an advanced technology of the future, artificial intelligence is very much a present-day reality. Thanks to inventions like self-driving cars, home assistant devices, automatic vacuum cleaners and remote home security solutions, Artificial Intelligence is on everyone's lips. Since AI seems to affect both the public and private sector, we started thinking about all the different ways in which it'll shake up the real estate world. Read on to find out more about the current and future impact of AI in property sales, marketing and operations. Artificial Intelligence, or AI for short, refers to smart technological tools whose level of awareness allows them to learn from their environment in order to improve processes and decision-making.
IBM announced it has added artificial intelligence (AI) to its TRIRIGA facilities management solution. TRIRIGA is an integration workplace management system, that helps customers manage their workspace and facilities. By utilizing existing real estate to its maximum potential, companies can reduce waste and save money on operating cost and real estate purchases. According to the press release, "the new TRIRIGA Assistant, a smart, conversational AI tool which uses natural language processing to help users quickly and easily engage with the spaces around them. TRIRIGA Assistant can help remove the hassle of coordinating with colleagues to schedule and reserve conference rooms, submit service requests such as lighting and catering, or locate a colleague's assigned workspace."
"By 2030, AI is predicted to add $15 trillion to the global GDP thanks largely to solving data issues according to PwC. Lending money used to be a tricky business but time consumers and technology is changing. Banks and other industries are struggling to cope with the changing consumer demand, but a few are getting it right…"
We use distributionally-robust optimization for machine learning to mitigate the effect of data poisoning attacks. We provide performance guarantees for the trained model on the original data (not including the poison records) by training the model for the worst-case distribution on a neighbourhood around the empirical distribution (extracted from the training dataset corrupted by a poisoning attack) defined using the Wasserstein distance. We relax the distributionally-robust machine learning problem by finding an upper bound for the worst-case fitness based on the empirical sampled-averaged fitness and the Lipschitz-constant of the fitness function (on the data for given model parameters) as regularizer. For regression models, we prove that this regularizer is equal to the dual norm of the model parameters. We use the Wine Quality dataset, the Boston Housing Market dataset, and the Adult dataset for demonstrating the results of this paper.
Valuations are relatively straightforward yet still involved exercises when similar properties in terms of hedonic variables[i] (also called comparables) transacted in the market close to the valuation date. In the absence of reliable comparable transactions, the possible value of a piece of real estate (be it residential or commercial) needs to be assessed using a valuation method. From back-of-the envelope cap rate models, transparent discounted cash-flow spreadsheets to sophisticated econometric models, any reliable valuation stands to benefit from accurate forecasts of expected levels of cash-flows and discount rates. The buying or selling decision is further influenced by the perceived current state of the real estate cycle but also the projected direction of the cycle. Predicting rents requires a good understanding of demand and supply forces at work in the space market, construction and how its financed, the evolution of the natural vacancy rate and possible migration flows of both firms and workers, among the more prominent determinants.