All Forum Posts by: Steven S.

Hi Adam, we do everything in-house from the permitting & architectural to the construction itself, so that removes a few variables. I got good at estimating renovation costs by making an initial estimate based on photos/walkthrough, drawing up a model like you see here, then once the project is finished, go to accounting, get the actual expense report for that property, and update the model. If there's differences, I investigate why, they are my models/estimates after all so they shouldn't be far off. Over time, I've got it down to being able to just look at photos to be within 5% of the actual construction cost. We do a lot of projects in the same markets so that helps.

To get cash, we have to finish the flip. We typically use Kiavi's hard money on the buy, cash when it needs to close ASAP, and we don't make any money until we sell the property. I wouldn't want to be doing a new BTR deal right now and ReFi at a 7-8% rate, unless of course it penciled.

When subbing out to other contractors, you can expect to pay 20-40% more than it would cost you to manage/do the work, so make sure you bake that into your model as well.

Quote from @David D.:

These are two different but related strategies it sounds like. Steven is using an ARV model that's homegrown, and ChatGPT is using Redfin's ARV model, probably with its own intuitions about repair values baked in there too.

These are still different from my approach though, I think? I'm assuming Steven is also using a small dataset of Comps. I've been skeptical of Comps since first hearing about them. If there's only a few comps you're using, there's naturally going to be a lot of error in your valuation model, so you're at greater risk of buying a home that really isn't going to net you much of anything. 

That's why I like this traditional ML approach, we leverage lots of data to inform our decision about which house is seriously "undervalued"

ChatGPT & any AI is just going to give you a summary of the web pages it can find, but it doesn't have all the historic data to calculate on, it's just regurgitating a web page it found. So not useful at all (unless you literally buy based on the RedFin/Zillow ARVs)

As for my tool, every transaction that occurs in my market area is another row in my database. I have tens of millions of rows, with data going back to 2000, for the LA/Ventura County area. It's so much that you can't open it in Excel (if you tried to... lol). 

From this source-data, I calculate everything you see in my above reply, and built things on top of that data for users to generate their own market stats, run comps, etc. The only way to value a home is by using sales comps, if you use any other method it's not going to be as accurate. So for your tool, you need exactly what I have, a transaction-level dataset, for your market, for your tools/programs to calculate on.

I showed you above you don't need Ai/ML to accurately determine the As-is and Renovated value. What you need is the source-data, a programmer to build you an app based on this data, and a experienced developer to instruct the programmer on how the algorithms/functions should perform these actions so they are actually useful. Without all 3 of these things readily available to you, it's a pointless endeavor.

You mention you like the traditional ML approach, where "we leverage lots of data to inform our decision about which house is seriously 'undervalued'", what would that be to you? Giving ChatGPT a 25GB file of your source data and having it go through it? Training your own AI on your transaction-level source data?

Quote from @Adam Taylor:

@Steven S. Hoping to achieve 8.5% - 9.5% once stabilized. I am looking into Maryland submarkets so like Frederick, Annapolis, North of Baltimore, etc. 

That seems very realistic, you should be able to build it into that. We get about the same out here in the San Fernando Valley. Loan rates on the ReFi have prohibited us from building a bunch more 3-4 unit properties recently, just kills the CF of the deal, but going all-cash on the purchase & construction still pencils.

 I second what Chris Seveny said, this Sensitivity/Stability is cool info to have, but isn't really used much. If you are pitching these models/investments to someone else, including this does make you seem more sophisticated & safer to go with than someone who doesn't, but the reality is they are rarely used to make actual decisions. I have found time is better spent building tools to quickly analyze & procure new deals, rather than hyper-analyzing the metrics of one deal, ultimately making initial review faster & more reliable (spend time on the things that make sense, quickly remove things that don't, even junior-level staff can identify good opportunities with the tools, etc).

For example, if I'm modeling a SFR to 3-4 unit scheme, I can quickly check the $/sf/mo for each of my proposed units and get a good idea of the GOI without having to manually dig through & find rent comps in that City/Zip:

- So instantly I know in Woodland Hills that a Studio is close to $5/sf/mo on avg, 1 Bedroom closer to $4/sf/mo, with the 2 & 3 Bedrooms closer to $3/sf/mo. The only other way to get this info is to run rent/lease comps yourself (~10-15min), or do enough projects in that area to where you have it.

Or for Thousand Oaks, etc:

Or if we have a choice between two projects in different markets, I can use this chart to compare the two rental markets:

-Basically anything to the right of 0 means the rent price/sf/mo has been growing QoQ, and the height is the current $/sf/mo of that market. I like to see 2%+ $/sf/mo appreciation & above $3/sf/mo on avg, indicates it's a higher-valued & safer market to put ~$1.5M in equity.

So you can definitely build these out, but beware it'll mostly be something you glance at and rarely get something actionable from. Building out tools for initial-review has been a much larger time & $ saver than these Stability/Sensitivity analysis charts.

That's the same info I could find on the RedFin link for the property, it's summary is great, but it's just nothing like what the guy here is trying to do

Quote from @Carlos Ptriawan:

Nice Steven....

And AI can give comps very fast as well these days lol :

Based on the recent home sales data for Woodland Hills, CA, it's difficult to determine if 22757 Charlemont Pl specifically has a good ARV (after repair value) without more details on the property's condition, size, and features compared to other recently sold homes. However, we can make some general observations:
The median sale price for homes in Woodland Hills was around $1.2-1.3 million in recent months, with prices trending up 4-7% year-over-year.
Homes are selling relatively quickly, with an average days on market of 35-44 days. Hot homes can sell even faster, in around 15-24 days.
The price per square foot averages around $640.
Example recent sales include:
$1,140,000 for a 4 bed, 3 bath, 2,228 sqft home on Oxnard St
$1,300,000 for a 3 bed, 2 bath, 1,936 sqft home on Hatteras St
$1,612,500 for a 3 bed, 2 bath, 2,161 sqft home on Oakdale Ave

Ai Comps... lol!

They are just pulling search results and copying the RedFin/Zillow/Realtor.com, etc estimated values/info on the pages:

And on Redfin, the exact same numbers:

So if you are just relying on RedFin/Zillow Estimates, then sure ChatGPT4 can help you with that, but anything more concrete/detailed and they can't (no historic $/sf of renovated homes available, only average/median, etc)

The % of ARV varies by market/area. I'm in Los Angeles, and the most I can pay on flips is typically 60-65%.

You want to get recently sold comps to value the property, it's the best way to be sure of the value. I usually use Redfin, but Canada isn't supported. Checked Zillow, you are right, only a few places have results in Canada. Perhaps there is a realtor association that has this information? Out here we call it the MLS, Multiple Listing Service. I'd try to call a few agents and see if they can run sold comps.

Once you have access to sold homes, there's nothing to get caught up on. Just go through them, select the renovated/closely matching ones, and enter them into a model.

Here's my fix & flip model, you can see the comps entered and some quick stats below them:

Then I use that comp data to adjust the Sale Price in the model to what I estimate it will be (double check the resale $/sf is in-line with the comps), then adjust the purchase price until I hit my desired return level. 

Good luck!

I do this right now. To identify a good market for me, I like to see good $/sf growth over the last 2 years, and a market that is $700/sf and higher. This chart basically shows me which City's are most desirable (can do the same with Zip Codes):

Anything to the right of 0 means the $/sf is growing QoQ on average, and the height is the current $/sf of that market. As you can see, there are a few City's that are at 5%+ QoQ $/sf growth, but lots more towards 0-3%. Instantly I can tell the hottest markets from the normal and cold ones, and separately determine which one's I am priced out of due to crazy high values.

As for your auto-ARV feature, yes it can be done. Take this 💥just-closed renovated property for example💥:

With my query of "22757 Charlemont Pl, Woodland Hills, CA 91364 5/4/2400-3050,0.6,300" we get an ARV Estimate that is ~4% off, and I picked this at random just scrolling through recently closed in my area

And the Market Stats $/sf of renovated property in that Zip Code matches up pretty well, so I know the ARV estimate is realistic:

I also set this up to run through every new-listing in my area, and generate a summary sheet of good-potential deals:

So it's all possible to do, you just have to get your data and build on top of it. Would be happy to help if you already have lined up a source of transaction-level data for your market (a row for each sale occuring, including the Close Price, Address, BR, BTH, SF, Lot SF, DOM, Lat, Lon, Listing Description, etc)

You don't need to, especially on deals that just make sense, but it's a good inclusion if you already have your underwriting model in Excel or gSheets because you can use all those variables, once filled out, to auto-populate various Sensitivity/Stability Analysis charts.

Here's what I have for Multifamily/Rentals (all auto-populated from the model):

On Fix & Flips, I only include the ARV Stability/Sensitivity Chart (this is the entire model, stability analysis at the bottom):

Since having these built out, it's something I mostly just glance at after underwriting to make sure everything looks good. It's caught some sensitivity a couple times where I otherwise wouldn't have realized, but nothing major. 

Often just the basics (Internal Cap Rate, DSCR, CoC, CF Rates), and disposition metrics (equity multiple, total return, annualized return, & GRM) are enough to determine a good deal.

Just curious, what kind of internal cap rates are you estimating for these 4-unit properties once fully built & stabilized?

I like to see good $/sf growth over the last 2 years, and a market that is $700/sf and higher. This chart basically shows me which City's are most desirable (can do the same with Zip Codes):

Anything to the right of 0 means the $/sf is growing QoQ on average, and the height is the current $/sf of that market. As you can see, there are a few City's that are at 5%+ QoQ $/sf growth, but lots more towards 0-3%. Instantly I can tell the hottest markets from the normal and cold ones, and separately determine which one's I am priced out of due to crazy high values.

I'd suggest a similar value-based approach if you have the data to calculate on to identify a few markets to start looking for deals in, but remember this is still too broad of analysis to make a go/no-go decision on the projects you find. You still have to run comps on everything, determine construction costs & estimate ARV, carry & commission costs, etc to make sure that deal in that market makes sense.

To do that last portion better, I use Sales Comps and drilled-down market stats. Here's a chart on 90066 I generated from my tool under Market Stats > Sales Data > Specific Zip Code:

- My above chart (more available) shows the Avg Renovated $/sf (High) over time in this Zip Code, something completely different than charts/providers based on Averages/Medians only, like every other platform (RedFin Below, https://www.redfin.com/news/data-center/):

- Can't select smaller locations such as City or Zipcode for their $/sf data, so it's useless helping me underwrite/verify a deal.

- Can set Zip Code for Median Sale Price data, but again it's the median/avg so it's useless helping me underwrite/verify a deal.

Then if that looks decent, I'll run Sales Comps, and get a quick summary of some key metrics based on those comps. Take this 💥just-closed renovated property for example💥:

With my query of "22757 Charlemont Pl, Woodland Hills, CA 91364 5/4/2400-3050,0.6,300" we get an ARV Estimate that is ~4% off, and I picked this at random just scrolling through recently closed in my area

And the Market Stats $/sf of renovated property in that Zip Code matches up pretty well, so I know the ARV estimate is realistic:

You can also run Agent Stats to find/get in touch with the real top-agents in that area so you can triple-verify everything and/or list it with them when you are done.

Quote from @Aaron Maxwell:

How do I find out which neighborhoods are up and coming on the internet. I know that I could look on YouTube news and I believe I can call planning and development but they only go so far. Is it propstream or can I find out another way. I know I can contact Realtors but I rather find out through black and white thank you guys.

Although you said you are in CA and want to invest elsewhere (I'd recommend something within a 1 hour drive from where you currently live), the process of picking the right market is the same.

I like to see good $/sf growth over the last 2 years, and a market that is $700/sf and higher. This chart basically shows me which City's are most desirable (can do the same with Zip Codes):

Anything to the right of 0 means the $/sf is growing QoQ on average, and the height is the current $/sf of that market. As you can see, there are a few City's that are at 5%+ QoQ $/sf growth, but lots more towards 0-3%. Instantly I can tell the hottest markets from the normal and cold ones, and separately determine which one's I am priced out of due to crazy high values.

I'd suggest a similar value-based approach if you have the data to calculate on to identify a few markets to start looking for deals in, but remember this is still too broad of analysis to make a go/no-go decision on the projects you find. You still have to run comps on everything, determine construction costs & estimate ARV, carry & commission costs, etc to make sure that deal in that market makes sense.