Price Optimization in Action

In cities like New York where vacancy rates often dip below 1%, the limited supply (landlords) maintains an upper hand over the seemingly unlimited demand (renters). Although the deck may be stacked in the landlord’s favor, we argue that cash is often left on the table as a result of how landlords choose what to charge for rent.

Landlords typically derive rental pricing in one of three ways:

  1. Call area brokers and ask about comparable units.
  2. Adjust last years price.
  3. Guess.

In short, rent prices are generally just an educated guess. Even though the technology exists for landlords to follow in the footsteps of airlines and hotels by using software for pricing, only 15% of the apartment industry has adopted it. The adoption lag is largely because the existing solutions are expensive and unwieldy. As such, the prevailing landlord notion is that the costs of implementing a revenue management system will outweigh the benefits.

The following case study uses real-world data to demonstrate the benefits of using a software solution to set rent prices more optimally.

The Building

  • Neighborhood: Lower East Side
  • 243 Units on 23 floors
  • Luxury rental with doorman
  • Opened 2008

Carrying Costs 

Since 2008, there have been 99 rental transactions in the building. We define carrying costs as the money lost for units left vacant. For each transaction, we multiply days-on-market times 1/30th of the monthly rental price. This comes out to a total cost of roughly $352k for just 99 transactions. This figure should have and could have been lower if more optimal prices were in place.  

The Cost of Setting the Rent Too Low

In addition to carrying costs, there is the cost of setting the rent too low (and having the apartment rented too quickly.) If the rent is not set optimally, then you will be stuck accepting below-market rent for up to year! So, if all of the prices are just 3% under their optimal price on average, the number of days that units sit vacant may be low, but this would come at a cost of 3% of income from rents.

Actual Prices versus Optimized Prices

imply comparing the opportunity costs of carrying an empty unit side by side does not really illustrate the significance of using optimized pricing. This is because any change in price will change effective gross income or the total expected income from rents less vacancy. In order to see the true significance of using optimized pricing, it was necessary to keep the number of days on market (a total of 2181 days) the same in both instances,although the number of days an individual unit would have sat on the market versus what actually happened may differ, resulting in the difference in carrying costs above.

What’s the Verdict?

We found that the “number of days on market” figure ranged from less than one day to almost four months. What this means is that the more overpriced a unit was, the longer it sat on the market. Similarly, the more underpriced a unit was, the quicker it moved. Taking this simple notion into account, let’s assume that the optimal number of days to wait for a highest bidder is 7 days. (We would need more data to calculate this exactly, but we suspect it would be in this range given what we know.) This would bring the total vacant unit-days down from 2181 to 693, and would yield a total revenue gain of:

In Conclusion

$341,908.81 of additional revenue over the course of three years for just 99 rental transactions is a compelling argument that a more sophisticated pricing system l is worth using. It will not only increase the bottom line, but also it will increase efficiency by saving time. With an appropriate pricing model in place, leasing agents can spend their time showing and renting units rather than spending time reviewing craigslist and playing around with excel just to end up with a price that isn’t optimal.  

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