How do you measure the success of the Hot Home feature in Redfin?

Before I begin answering the question, I would like to lay out the framework for the answer:
Structure: 
Clarifying questions
Goal
- How does this feature fit in?
Users
User Journey
Metrics
- North Star Metric(s)
- Supplementary Metrics

Clarifying questions:
1. Hot Home is a feature provided by Redfin that allows a prospective buyer to know that there are multiple other potential buyers who have seen the listing and there is likely to be increased demand for purchase and that the home may not be on the market for too long - Is that correct? - Yes
2. Are we measuring success for App/Web? - Both
3. I'm assuming that we are only looking at the US for this - Ok
4. Are there any constraints that I should know about? - No
5. What stage of development is the feature in? - MVP/Fulled Rolled out? - Assume Fully Rolled out.

Goal of Redfin - Redefine the way real estate is purchased or sold in favor of the Customer. We can clearly see that the 'hot home' feature is enhancing the mission statement by showing highly favored homes that benefit both the buyer and seller.

Let's think about some primary users here:
Home Seller
Home Buyer
Real Estate Agents (buying and closing agents)

From these, I would like to focus on the buyer side of the market as this feature will have the greatest impact on purchase decisions.
 

User journey - 
Open App
Set filters for Price range, location, type and size of houses etc.
See available inventory in the market
Review the ones that look interesting (Hot homes/ other houses that are of interest)
Understand the details on the house
Work with Agent to schedule a tour/open-house
Place bid
Request for inspection
Buy home
Pay agent cut

Metrics that are important to measure:
 Aquisition:
# Number of homes in a market that are designated as Hot Homes (as a percentage of overall listings)

Engagement:
# Average number of clicks on app/web for hot homes compared to other homes
# Number of Tours (requested and done) of Hot Homes compared to the average in that region
# Average number of bids placed on hot homes (compared to other homes in region)

Retention:
# Average number of days a Hot Home is on the market (listed to sold)
# Average time between Tour and bid placement

Monetization:
# Average selling price of Hot Homes compared to other similar homes
# Average price above listed price that hot homes sell for.

From these, I would consider the Following to be my top 2 NS metrics
1. Average number of clicks on app/web for hot homes compared to other homes 
2. Average number of days a Hot Home is on the market (listed to sold) 

These will give me an indication if the algorithm is correctly identifying the right homes as Hot Homes or not

Some of the pitfalls of these metrics could be a consequence of buyer FOMO. For ex: mutiple buyers may bid well over the asking price for Hot homes copared to other homes only because they fear missing out on a "socially' good home.

Potential Pitfalls: Given that hot homes are among the most in demand, some users may also be dettered from seeing it or bidding on it as they do not want to enter a bidding war and overpay. We may want to track those as well.

Some metrics that comes to mind are:
# buyers who clicked on a hot home but did not tour
# buyers who toured a hot home but did not bid