Real estate price and activity indices

A method for automated generation of a real estate price index, HPI, for a specified period of time using a computer system having a server computer coupled to a network and a local data storage device coupled to the server computer, wherein the server computer is configured to receive and store from external sources real estate sales prices of individual properties, n, within selected criteria and in a specific geographical area, wherein X is the real estate sales price in the specified period of time at date A and Y is the previous real estate sales price at date B, and the APGR and weighted APGR for each individual property is determined as follows:

• Step 1:
• Calculating the Annualized Price Growth Rate, called gi:
• g
• i
• =
• (
• X
• i
• Y
• i
• )
• 365
• A
• i
• -
• B
• i
• -
• 1
• Step 2:
• Calculating the sum of the sold prices for all above n properties, called V.
• V
• =
• ∑
• i
• =
• 1
• n
• 
• 
• X
• i
• Step 3:
• Calculating the Price Weighted APGR, called pi:
• p
• i
• =
• g
• i
• *
• X
• i
• V
• Determining the aggregate Price Weighted APGR for the n properties, called TG0:
• TG
• 0
• =
• ∑
• i
• =
• 1
• n
• 
• 
• p
• i
• TG0 represents 100 point for HPI at the month of t0:
• HPI0=100
• Wherein the HPI at the month of ti is calculated as below:
• HPI
• i
• =
• (
• 1
• +
• TG
• i
• 1
• +
• TG
• i
• -
• 1
• )
• *
• HPI
• i
• -
• 1
• *
• (
• 1
• +
• TG
• i
• -
• 1
• 12
• )
• Alternatively, this Formula can be written as follows:
• HPI
• [
• i
• ]
• =
• (
• 1
• +
• TG
• [
• i
• ]
• 1
• +
• TG
• [
• i
• -
• 1
• ]
• )
• *
• HPI
• [
• i
• -
• 1
• ]
• *
• (
• 1
• +
• TG
• [
• i
• -
• 1
• ]
• )
• 1
• 12

A method for automated generation of a real estate active index, HAI, for a month using a computer system having a server computer coupled to a network and a local data storage device coupled to the server computer, wherein the server computer is configured to receive and store from external sources real estate sales prices of individual properties, n, within selection criteria and in a specific geographical area, wherein X is the real estate sales price in the specified period of time at date A and Y is the previous real estate sales price at date B, and the APGR and weighted APGR for each individual property is determined as follows:

• Step 1:
• Calculate the sum of the sale prices for all n properties, called T0.
• T
• 0
• =
• ∑
• i
• =
• 1
• n
• 0
• 
• 
• p
• i
• and total number of sales is n0.
• Here n0 is called n-value at time t0, while T0 is called t-value at time t0.
• Step 2:
• Calculate the number of the total business days in the month, and let's assume there were total d0 business days in this month. Average working days per month is set up 21 (or 252 days divided by 12 months), d-value is calculated:
• 21/d0.
• Here it is called d-value at time to.
• Step 3:
• set up c-value as the geometric mean of n-value, t-value and d-value, or
• c
• 0
• =
• n
• 0
• *
• t
• 0
• *
• d
• 0
• 3
• Step 4:
• Set up HAI at the month of t0=100 (January 2017 HAI:
• 100 points)
• HAI0=100
• At t1, there were n1 properties of sales meeting the selection criteria of a qualified sales transaction, with sold prices as pi (here i is from 1 to n1), and there is total d1 of business days in this month and its n-value, t-value, and d-value are calculated at time t1 as follows:
• Step 1:
• Calculate the sum of the sale prices for all n properties, called T1.
• T
• 1
• =
• ∑
• i
• =
• 1
• n
• 1
• 
• 
• p
• i
• and total number of sales is n1
• At time t1, t-value=T1, n-value=n1
• Step 2:
• d-value is calculated as 21/d1 at the month of t1.
• Step 3:
• c-value is calculated as
• c
• 1
• =
• n
• 1
• *
• t
• 1
• *
• d
• 1
• 3
• Step 4:
• The Active Index is calculated as below:
• HAI
• 1
• =
• c
• 1
• c
• 0
• *
• HAI
• 0