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| December 22, 2015
TF Cornerstone is a partnership of two brothers, Tom and Fred Elghanayan, focused on acquisition, development, construction and management of residential, commercial and retail properties.
Article | March 4, 2020
When speaking with perspective investors, the first question that I ask is how well they understand the numbers of a potential investment. If they reply with a simple, “everything’s good,” I know the deal probably isn’t for me. But, if they respond with hard data about the market, property and financial information, I can take them seriously and consider the investment. As an investor in any industry, numbers are your best friend. I’ve spent hours pouring over spreadsheets of data and mastering best accounting practices and it’s made me the diligent investor that I am today. I believe that this dedication to digging into data is what sets met apart from other real estate investors and continues to give me an edge.
Article | March 8, 2020
Financial modeling is a technical application as old as finance itself. Traditionally performed through excel sheets and various DCF models, I wanted to set out and use Machine Learning to build a predictive model for the movement of an asset’s price. Within a two week project time period, I set out to analyze and build predictive SARIMAX (Seasonal Auto-Regressive Integrated Moving Average with Exogeneity) models to capture the movement of eight different Real Estate Investment Trusts (REITs). Before we get into the data, it is essential to first understand the nature of a REIT. REITs are just like any other financial asset in that ownership represents a “share” of a companies profit. These companies own and operate income-producing real estate and are usually centric to a particular sector of real estate type (data centers, retail properties, Senior/Assisted living communities, healthcare, etc…). There are two main types of REITS:
Unlike last year, the residential real estate market kicked off 2020 with a bang! In their latest Monthly Mortgage Monitor, Black Knight proclaimed. The housing market is heating entering 2020 and recent rate declines could continue that trend, a sharp contrast to the strong cooling that was seen at this same time last year. Zillow revealed they’re also seeing a robust beginning to the year. Jeff Tucker, Zillow Economist, said. Our first look at 2020 data suggests that we could see the most competitive home shopping season in years, as buyers are already competing over…homes for sale. Buying demand is very strong. The latest Showing Index from ShowingTime reported a 20.2% year-over-year increase in purchaser traffic across the country, the sixth consecutive month of nationwide growth, and the largest increase in the history of the index. The even better news is that buyers are not just looking. The latest Existing Home Sales Report from the National Association of Realtors (NAR) showed that closed sales increased 9.6% from a year ago. This increase in overall activity has caused Zelman & Associates to increase their projection for home price appreciation in 2020 from 3.7% to 4.7%.
Article | February 21, 2020
Over the past few years, data has become the most discussed topic in commercial real estate (CRE). Like almost every other field, the proliferation and access to data is fundamentally changing the way the world thinks about commercial properties and commercial real estate in general. And like other industries, one of the critical challenges with the emergence of real estate data is the ability to understand and communicate what we actually mean when we lump every bit of information into a generic term like “data.” Depending on who you speak with in the real estate space, data can mean a variety of different things. For some in commercial real estate, data is all-encompassing and includes every bit of information available about a property. For others, data includes information about how the building works and all of the relevant systems that power a modern building. And yet for others in commercial real estate, data is everything captured around a transaction related to a specific piece of property.
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