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Insights on how to use AI in real estate for property managers, investors, and brokers.
I wanted to write this series of posts to answer each of the most common questions we hear in demos, and to give some specific examples of how our approach delivers the best rent comps, highly accurate expense benchmarks, and a greatly accelerated multifamily market analysis process.
One important data point for any multifamily analysis is Net Effective Rent (NER). This guide will demystify net effective rent, discuss its importance in multifamily properties, and introduce some indispensable tools and calculators that can help property managers and investors make informed decisions.
Whether it’s for a multifamily property or single-family rental (SFR), every rental property investor wants to collect the highest rent possible to maximize the value of their investment. But it can be difficult to find good sources of rental market data, and challenging to determine which properties are truly relevant rent comps for your investment.
Fair Market Rent (FMR) is a term used in the United States to describe the amount of money that a property would rent for on the open market. It's often used in the context of various housing and rental programs, including those overseen by the Department of Housing and Urban Development (HUD).
With recent advancements in artificial intelligence, there are many ways multifamily real estate investors can leverage AI to enhance decision-making, improve operational efficiency, and maximize returns. But at the same time, it is a LOT to process – it can be difficult to know where to start with AI.
Using computer vision to identify value-add deals in the multifamily real estate sector can be a game-changer. Value-add deals refer to properties that offer the potential for increased returns through various enhancements, such as physical improvements, operational changes, or market position improvements.
As the real estate data science team at Hello Data has been analyzing rent and time on market from apartments across the country, we’ve noticed an interesting phenomenon in some properties… they keep a few units on the market seemingly forever.
Prompt libraries serve as centralized, categorized repositories of prompts that enhance the efficiency and reliability of AI models. They are particularly useful for tasks like image and text generation. This article highlights added functionalities like version control and data governance that make these libraries invaluable.
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