All Forum Posts by: Ariel Herrera

Learning programming is a competitive advantage in real estate to obtain quality data, automate processes, and analyze deals at scale. As a Data Scientist in the real estate sector I am often asked, how do I analyze multiple markets? how do I get data from Zillow? how do I use creative methods to obtain leads? Simple answer - learn a scripting language FAST and work on REAL WORLD problems.

I created a course, Introduction to Real Estate Data Analytics, to help those who want to learn their market and get started in Python. It focuses on using real estate related examples. The beginning of the course is available for free on Youtube.

What other resources do you suggest for real estate folks to get started in programming?

I work as a data scientist with big data in financial industry, self-taught programming

Yes, I recently completed a live and rent where I put down 5% for a conventional loan, held for a year, rented, and now have positive monthly cash flow.

I have a video that explains why you should consider South Amboy as an area to invest in. To summarize top 4 reasons are location, jobs, investor strategies, and growth in terms of local development in the area.

Video link

Machine learning can be a great tool for real estate investing. Is it the solution for EVERY aspect of finding a great deal? No. However, based on the data you have there could be a lot of use cases to streamline that process.

A great resource for data is using an API, I like realtor API for information on multi-family properties - https://rapidapi.com/blog/best-real-estate-apis/.

Benefits of ML:

- Removes the manual effort of filtering and searching

- Can find patterns in large sets of data

- Adaptable to changing environments

- Results in actionable insights

Some ML use cases:

- Prediction - Identify a home's value price based on a dataset of comparables (similar to zillow's zestimate). This could help when placing an offer on a property. It saves time and effort of digging through comparables manually, especially if you want to look at more than one area at a time.

- Clustering - Group different multi-family properties together based on similar features. You can quickly assess which properties you're interested in. Label those properties. Then train a model to tag similar properties and have an automated email sent to you.

- Natural language processing - Use web scraping to get information on wholesaler websites, municipality websites, and forums for latest market data. Distill that text data with natural language processing. Highlight possible new markets or properties to look into.

These are just a few use cases. The main reason I advocate using ML is that it serves as another tool to help systemize your processes. If you're looking to expand and grow using systems is essential. Technology is a faster and cheaper way to do so.

@Sebastian Rodriguez thank you for the advice, I agree I should let the banks money work for me while saving for next investment. Maybe we could collaborate for the next one!

Ok great that makes sense for the strategy of scaling rental properties, thank you so much for the insight!

I recently purchased my first home with 5% down. I am house hacking by having a roommate pay a portion of the rent. 

After the mortgage, utilities, and other expenses I estimated that I can save about 30% of my salary earnings after taxes. My goal is to buy another property in 2-3 years to either live in (then rent 1st property) or rent out (then stay in 1st property). 

I am concerned if I save enough to put 20% down on a property 2 years from now but still have low equity in my current home how that will affect my ability to get a loan.

Should I use the savings to apply extra payments to the mortgage to have more equity (20%) ? Or would it be more beneficial to pay the mortgage as is and save solely for the future investment property?