<p>I am looking to calculate the equilibrium price, i.e an optimal price that I can set without affecting demand and maximize revenue.</p>

<p>I've gathered historical data: occupancy rates, asking rents for different property types by submarket / market and have a bunch of other data including local economic variables, new construction, location characteristics etc. Here's how I am thinking about estimating the demand function -</p>

<p>First predict demand, occupancy rates using Regression with fixed effects and historical prices for a particular property type in a given submarket. The training data for this is last 5+ years of historical data.</p>

<p>Approx. Model specification:</p>

<pre><code>Dp = bo + b1*(Econ) + b2*Supply + b3*Loc + b4*Price t + b5*Price t-1 + b6*Price t-2
</code></pre>

<p>With this, I can get an estimate of how much price can change without significantly affecting quantity demanded. The assumption here is all else being equal. However, supply/vacancies changes.</p>

<p>So, my question is how would you model this? Are there any other analytical/ML approaches to model this and calculate equilibrium price?</p>

CC BY-SA 4.0
2020-06-10T13:17:50.263Z
It could be that the level of supply has little effect on quantity demanded in a poor location, but a considerable effect in a good location. To allow for this sort of effect you might consider including some interaction terms or trying a logged functional form
Adam Bailey
CC BY-SA 4.0
2020-06-10T13:22:17.223Z
Good point. Location characteristics and demographics (median income, etc) will capture some of these differences. Otherwise, I could consider classifying submarket as good, bad, average etc. and then use that interaction term?
kms
CC BY-SA 4.0
2020-06-10T13:30:20.970Z
Yes, a dummy variable classifying submarkets to use in an interaction term could be helpful.
Adam Bailey
CC BY-SA 4.0
2020-06-10T13:56:14.220Z
Any thoughts on how you'd interpret the coefficients? i.e the effect of price on demand. Given, all else being equal is not a realistic assumption, in this case. The location and demographics are fixed effects, however, competitor price and supply may vary with time.
kms
<supply-and-demand><regression><optimization><equilibrium-price>
Analytical approach to estimate equilibrium price for Real Estate Property
kms
# Analytical approach to estimate equilibrium price for Real Estate Property

(Asked by: username_0 on 2020-06-10)

I am looking to calculate the equilibrium price, i.e an optimal price that I can set without affecting demand and maximize revenue.

I've gathered historical data: occupancy rates, asking rents for different property types by submarket / market and have a bunch of other data including local economic variables, new construction, location characteristics etc. Here's how I am thinking about estimating the demand function -

First predict demand, occupancy rates using Regression with fixed effects and historical prices for a particular property type in a given submarket. The training data for this is last 5+ years of historical data.

Approx. Model specification:

```
Dp = bo + b1*(Econ) + b2*Supply + b3*Loc + b4*Price t + b5*Price t-1 + b6*Price t-2

```

With this, I can get an estimate of how much price can change without significantly affecting quantity demanded. The assumption here is all else being equal. However, supply/vacancies changes.

So, my question is how would you model this? Are there any other analytical/ML approaches to model this and calculate equilibrium price?

**Tags:** supply-and-demand, regression, optimization, equilibrium-price

> **Comments:**
> **username_1**: It could be that the level of supply has little effect on quantity demanded in a poor location, but a considerable effect in a good location. To allow for this sort of effect you might consider including some interaction terms or trying a logged functional form  
> **username_0**: Good point. Location characteristics and demographics (median income, etc) will capture some of these differences. Otherwise, I could consider classifying submarket as good, bad, average etc. and then use that interaction term?  
> **username_1**: Yes, a dummy variable classifying submarkets to use in an interaction term could be helpful.  
> **username_0**: Any thoughts on how you'd interpret the coefficients? i.e the effect of price on demand. Given, all else being equal is not a realistic assumption, in this case. The location and demographics are fixed effects, however, competitor price and supply may vary with time.  

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## 0 answers

