Conducting data analytics for mortgage brokers firms

Key quotes:

• "Existing rental space concepts and office layouts need to be rethought and commercial real estate landlords will likely have to shift to a more services-oriented model, where the core product is not only the physical space but a value-added service offering around it."
• "Currently, a number of companies are trying to democratise real estate investing, granting investors with less liquidity the opportunity to participate in asset appreciation through digital solutions."
• "Existing solutions, for instance, offer a co-investment model, enabling investors to participate in real estate transactions traditionally only available to institutional investors."
• "While this scenario doesn’t offer the opportunity of potential asset appreciation, providing debt has the benefit of a fixed interest rate and loan term, creating clear parameters for participating investors."
• "TripleMint, a btov portfolio company, is a software-powered real estate brokerage that is changing the way people buy, sell and rent homes."
• "Although we likely will see a short-term contraction in real estate prices, we believe the trend of rising rents will continue in the long run."

Key quotes:

• "Don’t miss our first post here where we conducted some exploratory data analysis on the application_train.csv and created some baseline models All the code for this post can be found here & model results, figures, and notes can be found in this Comet.ml public project In our first post, we extracted a subset of candidate features from a single csv file, application_train.csv, and trained some baseline models with this data (our LightGBM model had an AUC score of 0.745)."
• "Thanks to Clement Kao, a product manager at Blend (a mortgage technology provider), we were able to connect with two mortgage professionals at Preferred Financial Group, Lead Processor Cody Dadiw and Senior Loan Officer Philippa Stewart-Donnelly! We asked Cody and Philippa to look through the different feature options in the column_description.csv (across all seven datasets) and select the features they thought would be indicative of an applicant’s default risk."

Key quotes:

• "Our vision at Landbay is to be the leading buy-to-let mortgage lending platform — that means being the go-to lender for brokers and the partner of choice for investors in these mortgages."
• "A good example of this is our stratification reporting capabilities — looking at volumes of mortgages over metrics such as loan size, LTV, region, rate etc."
• "Stratification reporting is a key measure for risk management mortgage portfolios and Landbay’s data solutions enable us to review these types of metrics in real time and break them down by funder to report on risk held in funded mortgages."
• "This enables us overlay new and offered loans on top of the completed mortgages enabling us to analyse the future stratifications and act to manage the shape of our portfolios by changing origination plans and allocations to optimise portfolios."
• "The growth of both marketplace technology and specialist BTL lending points to continued significant growth for Landbay."

Key quotes:

• "The calculation is based on variety of public data records, market information, user data points by using Big Data analysis formula developed in-house."
• "Additionally, they encourage consumers and homeowners to expand online data by doing other investigations such as comparing market prices for around areas, working with a real estate agent, getting an appraisal from an expert and visiting the house."
• "The analysis combines several main components, including property characteristics in the area, crime rate, quality of life, pollution, race and ethnicity, population growth, family household, house value, business field, employment, schools and future home value."
• "Latest Big Data collections made it all possible for real estate website to provide that information to potential buyers."
• "Based on the data visualization, population size in Tarzana is higher by 3,000 residents than in Calabasas, as shown in Figure 6."
• "Real estate agencies, such as Zillow, estimate values based on Big Data collection tools and using their algorithm.They combine information from variety of sources and provide insightful information to buyers, sellers or brokers."

Key quotes:

• "New analytical tools with predictive capabilities will dramatically affect the future of urban development, reshaping the real estate industry in the process."
• "Real estate tech companies such as SpaceQuant, Mashvisor, SMartZip, Enodo Score, and Zillow all rely on large datasets for simulating future investment outcomes or assessing potential market trends."
• "New algorithmic logic enables predictions based on datasets aggregated over the past 20 years by government websites or large real estate data platforms such as CoStar."
• "By looking at the statistical meaning of the data, the real estate industry is now invited to look at the probability of deals success.” If predictive capabilities open new horizons to urban development and real estate, data accuracy is the heart of the matter."
• "After interviewing Jasjeet Thind [4], VP of Data Science at Zillow, the explanation is clear: the number of datasets is such that processing them and adapting the infrastructure to their ever-growing size makes it hard to achieve long time span prediction, accuracy, and a large set of property types."