All Forum Posts by: Steven S.

ChatGPT agrees & will explain things a little more: https://chat.openai.com/share/...

Hi Luis, I'm not a wholesaler but I'm basically a robot who talks in numbers and you got a lot in there. Also your caps-lock key is on.

Wholesaling is all about knowing ARV, you have to know what this thing will be worth once fully built-out/renovated, because with that, you know what Mr Developer will sell it for.

Your 0.7x the ARV is estimating what this developer will pay for the property (and tells me this will be a very simple fix & flip with no major construction, not much room there for Mr Developer to run into unexpected issues, or you are subtracting the construction costs later). I know this because I buy fix & flips for ~60-65% of the ARV and do the full rehab, 70% would be tight (sold these flips last month: https://www.redfin.com/CA/West..., https://www.redfin.com/CA/Los-... )

Developers are usually hungry to make ~20%+ profit on a 6 month flip. So if you are able to estimate the ARV, and then the total construction, carry, and sale-side costs (5%) for the finished project, you can then figure out how much Mr Investor would pay for this project, the rest should be your wholesale fee (20% developer profit offer price - seller asking price = wholesale fee). This approach gives you the ability to charge your highest possible wholesale fee, since you know what a developer would pay for this to fix and flip it and make their 20%.

Your $104,628 offer to seller with your wholesale fee of $40k means the flipper pays $144,628 for a house, that after a reno will sell for $278,040. You left money on the table for sure, or gave it to the developer rather because they bought at 52% of the ARV.

Your $174,400 offer to seller with your wholesale fee of $40k means the flipper pays $214,400 for a house, that after a reno will sell for $278,040, might be too high for a developer to be interested (definitely below 20% profit after the reno, carry, and selling costs, if not upside-down) because they bought at 77% of the ARV.

That's how I would rationalize it, and get the most out of every seller you lock-up with. Keep in mind I'm out in the LA/Ventura area, adjust your developer profit % margin for what is regular out in your market, then apply the same process.

You know RedFin/Zillow is syndicated with most MLS's, right? I'm curious why you recommend only the MLS for comps, since that is a tool made only for licensed Real Estate Agents to use. It doesn't sound like Kyle is a realtor, rather it sounds like you want him to use one without learning how to run comps himself, which was his request.

Sold homes from Redfin out in near you in Belton, TX (for example):

Quote from @Chris Morris:

Quote from @Steven S.:

Quote from @Chris Morris:

Quote from @Steven S.:

Quote from @Chris Morris:

How do you log in and test it out for free?

Go to app.runcomps.org, enter the key from inside the 'quotes' below:

LA/Ventura data: 'your_best_friend'

SF/Bay Area data: 'robby the man in sf'

Thanks. It's nice to see cost per square foot broken down by bedroom count. Where do you get your data? 

Thank you! I really tried to make these tools plug-and-play into underwriting models.
Most public or private tools (like Zillow monthly snapshots or RefFin market trends) often only provide averaged, non-segmented, data (like the chart I included in my original post with just average $/sf & absorption plotted). Don't get me wrong, that's good for broad interpretations of the market or detecting where things are heading in general, but I needed drilled-down tooling to really fill out & validate both my MF & SFR underwritings. Such as the rental-data chart you mentioned, or this sales-data chart below to segment the $/sf of closes over time by the market tiers (High = top-of-market closes i.e. Highly renovated, Low = bottom-of-market closes i.e. Distressed or as-is):

Data comes from free public sources that my scripts clean, format, and join together. The front-end app I provided lets you perform calculations on & make charts from this database, essentially.

 Got it. So what's happening when I select 'Sales Comps', enter a specific address, and *also* specify bed/bath count and square footage range? Is the address used just to give a location to search within, but then the comps to check are based on the bed/bath count and square footage range?

And how do you come up w/ a 'distressed' calculation and 'renovated' calculation? Do you classify each property as one or the other based on its delta from the median price, or is there a 'hard' signal to indicate if a property was renovated?

The address you provide is passed to opencagedata's API to get the Lat & Lon coordinates of that address passed back to my program. Then your Radius & Days Back parameters cut down the full database to what is relevant to your search. Then the remaining transactions are filtered by the rest of your paramters (BR/BTH/SFmin-SFmax). If there are enough comps after that, you get your results. If not, there is a bit of fuzzy searching that will expand the filter parameters to try and get the minimum required number of comps to make a prediction & give you some results, but if you notice they are expanded much too far I would open up the radius & days back to try and get more representative results.

The High, Mid, and Low market tiers are quantile groups based on the $/sf of every transaction that occurred during that Quarter. So if closes in Q1 ranged from $350/sf to $900/sf in 91367, the High-Tier would be all the transactions that fall under +-15% of $900/sf (averaged together), with the Low-Tier being +-20% of the $350/sf transactions (averaged together), etc. The %'s are not exactly as-coded, but that should give you the idea.

I can only speak to what I do, but what I've learned is everyone has their own risk tolerance, and that is really where the 'accepted' underwriting methods come from. Some investors would rather underwrite the deal every which way until next Tuesday, and be ok with missing out on a deal should another buyer come in before they fund the deal. Some don't check at all and take a local or the selling agent's word at face-value since they will re-list with them or know them.

The big boys club of doing it better than local agents is a façade, look at Opendoor's or Zillow's home-buying & flipping businesses, they almost always have LOST money in their efforts, trying to do this mass-market acquisitions & analysis. I think Opendoor posted a profit finally this year, but very miniscule: https://investor.opendoor.com/...

So I second what @Doug Smith said, but it will always be crucial for YOU, the investor/buyer, to run your own numbers to make sure no one is pulling your leg. After all, it is your money on the line!

As for how I developed RUNCOMPS, the data comes from free public sources that my scripts clean, format, and join together. The front-end app I provided lets you perform calculations on & make charts from this database, essentially. 

Thank you! I really tried to make these tools plug-and-play into underwriting models.
Most public or private tools (like Zillow monthly snapshots or RefFin market trends) often only provide averaged, non-segmented, data (like the chart I included in my original post with just average $/sf & absorption plotted). Don't get me wrong, that's good for broad interpretations of the market or detecting where things are heading in general, but I needed drilled-down tooling to really fill out & validate both my MF & SFR underwritings. Such as the rental-data chart you mentioned, or this sales-data chart below to segment the $/sf of closes over time by the market tiers (High = top-of-market closes i.e. Highly renovated, Low = bottom-of-market closes i.e. Distressed or as-is):

Data comes from free public sources that my scripts clean, format, and join together. The front-end app I provided lets you perform calculations on & make charts from this database, essentially.

I actually just posted a whole guide about how I do it at scale, click my profile to pull it up. I've underwritten thousands of deals.

You can use RedFin, Zillow, or any Transaction-level data source. Draw a boundary around the properties neighborhood/sub-market (0.25 - 0.75mi usually). Filter last 6 months, filter for similar BR/BTH with +-5-10% of the square footage, sort high to low, and then you have a list of the top-tier closes for that housing product (hopefully they are renovated if that's your end goal), with the list decreasing in value as you scroll down. This will give you a really good idea of the ARV and the as-is value, to make sure you are in the right place on both. Then start to get into the details, pool or no pool, views or no views, etc to make sure the Comps you are selecting properly reflect your subject property once it is renovated.

Oh, and it's never bad to check Active & Pending comps with those same filters once you are done with the Sold comps, to make double-sure the product you will be delivering will be where you think. I've called many on-market comp agent's to discuss the buyer activity before I put the EMD in on mine

Go to app.runcomps.org, enter the key from inside the 'quotes' below:

LA/Ventura data: 'your_best_friend'

SF/Bay Area data: 'robby the man in sf'

Here's a recording of how you can use the Market Stats to validate your ARV projections: https://www.youtube.com/shorts/SMZ0pidg1-Y

I wasn't either until:

(1) I had to manually get data & make customized charts similar to this in excel for our senior capstone Investor Brochure's Market Analysis section on our proposed 378-door mixed-use project in North Park, San Diego (the site is now was supposedly being built, called The Winslow, good capstone program)

(2) We had a lot of deal flow, had to develop something to help analyze them, and help make sense of this market, things don't always go up

I wish I had this during #1, and realized I needed it during #2