22.17 Real Estate

By Ravindra Bagale Last updated: 6 October 2026

Site interviews ask how many visits became a booking, how fast a listed unit sold (absorption), and days on market. Brochure downloads and raw site visits are the vanity pair. Sites below are fictional plots for class, not a developer's inventory.

KPI	What the number means	Who uses it	Action it drives
Visit-to-booking %	Bookings divided by site visits.	Site head	A low rate with many visits is follow-up or price, not "bring more people".
Absorption	Units sold divided by units listed.	Sales head	A site that lists 12 and sells 2 is stuck stock.
Days on market	Sum of days from list to sale, divided by units sold.	Sales head	A long average is a price or a channel problem. Unsold units are not in this average. Say that.

Fictional sample rows. One quarter. Type these rows into a new sheet from cell A1, press Ctrl + T, and name the table tblSites (Table Design › Table Name).

Site	City	Visits	Bookings	Listed	Sold	Sum of days on market
Pune West	Pune	40	8	10	4	120
Pune East	Pune	80	4	12	2	200
Nashik Road	Nashik	30	6	8	4	80
Gangapur	Nashik	20	5	6	3	60
Kolhapur	Kolhapur	50	2	10	1	90
Nagpur	Nagpur	24	6	8	4	100

Excel formulas for the whole file (type them in empty cells below or beside the table):

KPI	Excel formula	Result	Number format
Visit-to-booking %	`=SUM(tblSites[Bookings])/SUM(tblSites[Visits])`	12.7%	`0.0%`
Absorption	`=SUM(tblSites[Sold])/SUM(tblSites[Listed])`	33.3%	`0.0%`
Days on market	`=SUM(tblSites[Sum of days on market])/SUM(tblSites[Sold])`	36.1 days	`0.0`

Calculated columns (one result per row). Click the first empty column of the table, type the header, then the formula. The Table fills every row by itself.

New column	Formula in the first row
Visit-to-booking %	`=[@Bookings]/[@Visits]`
Absorption	`=[@Sold]/[@Listed]`
Days on market	`=[@[Sum of days on market]]/[@Sold]`

Expected result per row:

Site	Visit-to-booking %	Absorption	Days on market
Pune West	20.0%	40.0%	30.0 days
Pune East	5.0%	16.7%	100.0 days
Nashik Road	20.0%	50.0%	20.0 days
Gangapur	25.0%	50.0%	20.0 days
Kolhapur	4.0%	10.0%	90.0 days
Nagpur	25.0%	50.0%	25.0 days

One city with SUMIFS. Visit-to-booking % for Pune only:

=SUMIFS(tblSites[Bookings],tblSites[City],"Pune")/SUMIFS(tblSites[Visits],tblSites[City],"Pune")

Result for Pune: 10.0%. Change "Pune" to a cell reference (for example $K$1) and add a drop-down, and the same formula answers for any city.

	Good KPI	Vanity metric
From the same rows	Pune East booking 5.0% and 100 days on market	Pune East 80 visits
Why	The busiest gate sold 2 of 12 units.	A weekend footfall photo looks like demand. Nashik Road had 30 visits and sold 4 of 8.

What the manager does. The Pune site head stops the visit drive at Pune East and reviews price and the 76 visits that did not book. The Nashik site head keeps the Nashik Road follow-up (half the listed units sold, 20 days). Gangapur is the same shape at a smaller scale: 5 ÷ 20 = 25.0% visit-to-booking, 3 ÷ 6 = 50.0% absorption. Kolhapur sold 1 of 10. Do not average Kolhapur into "Maharashtra is fine" because Nagpur sold 4 of 8.

Practice workbook and guide: Real Estate dashboard workbook (Excel) · How to build the Real Estate dashboard in Excel. The workbook data is also fictional.

Ravindra Bagale's Tip

Days on market is an average over units sold only. Leave unsold units out of the denominator and list them as "still open".