National Retail Properties Inc (NNN)
National Retail Properties Inc. (NNN) is a premier real estate investment trust (REIT) focusing on the acquisition and management of high-quality retail properties across the United States, which are typically subject to long-term net leases. With a diversified portfolio that includes well-known national and regional retail brands, the company aims to provide stable and predictable cash flows while delivering value for shareholders. NNN's disciplined investment strategy and commitment to portfolio quality position it as a leading player in the net lease sector, catering to the growing demand for high-quality retail space in a dynamic market.
Stock Price Trends
Stock price trends estimated using linear regression.
Key facts
- The primary trend is increasing.
- The growth rate of the primary trend is 29.17% per annum.
- NNN price at the close of November 19, 2025 was $40.62 and was lower than the bottom border of the primary price channel by $12.65 (23.74%). This indicates a possible reversal in the primary trend direction.
- The secondary trend is increasing.
- The growth rate of the secondary trend is 4.81% per annum.
- NNN price at the close of November 19, 2025 was inside the secondary price channel.
Linear Regression Model
Model equation:
Yi = α + β × Xi + εi
Top border of price channel:
Exp(Yi) = Exp(a + b × Xi + 2 × s)
Bottom border of price channel:
Exp(Yi) = Exp(a + b × Xi – 2 × s)
where:
i - observation number
Yi - natural logarithm of NNN price
Xi - time index, 1 day interval
σ - standard deviation of εi
a - estimator of α
b - estimator of β
s - estimator of σ
Exp() - calculates the exponent of e
Primary Trend
Start date: August 2, 2023
End date: October 30, 2024
a = 2.7853
b = 0.0007
s = 0.0450
Annual growth rate:
Exp(365 × b) – 1
= Exp(365 × 0.0007) – 1
= 29.17%
Price channel spread:
Exp(4 × s) – 1
= Exp(4 × 0.0450) – 1
= 19.70%
August 2, 2023 calculations
Top border of price channel:
Exp(Y676)
= Exp(a + b × X676 + 2 × s)
= Exp(a + b × 985 + 2 × s)
= Exp(2.7853 + 0.0007 × 985 + 2 × 0.0450)
= Exp(3.5660)
= $35.37
Bottom border of price channel:
Exp(Y676)
= Exp(a + b × X676 – 2 × s)
= Exp(a + b × 985 – 2 × s)
= Exp(2.7853 + 0.0007 × 985 – 2 × 0.0450)
= Exp(3.3862)
= $29.55
October 30, 2024 calculations
Top border of price channel:
Exp(Y990)
= Exp(a + b × X990 + 2 × s)
= Exp(a + b × 1,440 + 2 × s)
= Exp(2.7853 + 0.0007 × 1,440 + 2 × 0.0450)
= Exp(3.8851)
= $48.67
Bottom border of price channel:
Exp(Y990)
= Exp(a + b × X990 – 2 × s)
= Exp(a + b × 1,440 – 2 × s)
= Exp(2.7853 + 0.0007 × 1,440 – 2 × 0.0450)
= Exp(3.7053)
= $40.66
Description
- The primary trend is increasing.
- The growth rate of the primary trend is 29.17% per annum.
- NNN price at the close of November 19, 2025 was $40.62 and was lower than the bottom border of the primary price channel by $12.65 (23.74%). This indicates a possible reversal in the primary trend direction.
Secondary Trend
Start date: November 27, 2023
End date: November 19, 2025
a = 3.5010
b = 0.0001
s = 0.0515
Annual growth rate:
Exp(365 × b) – 1
= Exp(365 × 0.0001) – 1
= 4.81%
Price channel spread:
Exp(4 × s) – 1
= Exp(4 × 0.0515) – 1
= 22.87%
November 27, 2023 calculations
Top border of price channel:
Exp(Y757)
= Exp(a + b × X757 + 2 × s)
= Exp(a + b × 1,102 + 2 × s)
= Exp(3.5010 + 0.0001 × 1,102 + 2 × 0.0515)
= Exp(3.7459)
= $42.35
Bottom border of price channel:
Exp(Y757)
= Exp(a + b × X757 – 2 × s)
= Exp(a + b × 1,102 – 2 × s)
= Exp(3.5010 + 0.0001 × 1,102 – 2 × 0.0515)
= Exp(3.5400)
= $34.47
November 19, 2025 calculations
Top border of price channel:
Exp(Y1,254)
= Exp(a + b × X1,254 + 2 × s)
= Exp(a + b × 1,825 + 2 × s)
= Exp(3.5010 + 0.0001 × 1,825 + 2 × 0.0515)
= Exp(3.8391)
= $46.48
Bottom border of price channel:
Exp(Y1,254)
= Exp(a + b × X1,254 – 2 × s)
= Exp(a + b × 1,825 – 2 × s)
= Exp(3.5010 + 0.0001 × 1,825 – 2 × 0.0515)
= Exp(3.6331)
= $37.83
Description
- The secondary trend is increasing.
- The growth rate of the secondary trend is 4.81% per annum.
- NNN price at the close of November 19, 2025 was inside the secondary price channel.