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Iowa Farmland Value 

Study 2024

(Farmland Value Trends and Technical Price Analysis)

Prepared by:  Trent Klarenbach, B.S.A., PAg

Klarenbach Research

www.klarenbach.ca

July 22, 2025

1. Executive Summary

This report examines historical Iowa farmland values through an inflation-adjusted lens, combining technical analysis tools using a logarithmic scale, Relative Strength Index (RSI), moving averages, Fibonacci Retracement, support and resistance analysis and Elliott Wave Theory.

The analysis is based on the USDA NASS farm real estate values, ISU Land Value Survey (1941-2024), which converted nominal farmland values to 2015 dollars using The U.S. Bureau of Labor Statistics: BLS historical CPI-U data. 

Key findings include:

• A steady, long-term upward trend in inflation-adjusted land values with cyclical corrections.

• The analysis identifies distinct historical cycles, including a significant boom in the 1970s, followed by a severe correction that lasted seven years (approximately 1979-1986). 

• Technical indicators (RSI and moving averages) highlight periods of potential market overextension and subsequent support from historical resistance levels.

• Historical declines (e.g., 1922–1933, 1979–1986) and technical indicators, combined with recent bearish divergence between the price and RSI suggest a potential correction in the near term.  

• The price trading below the 2-year Exponential Moving Average suggests a possible trend reversal leading to further declines.

The findings suggest that technical signals offer nuanced insights into cyclical patterns and preceding catalysts, including interest rate hikes, shifts in commodity prices, and policy changes.

2. Introduction

Iowa’s farmland has long been a cornerstone of United States agriculture, reflecting both local and global economic trends. 

This study aims to provide a robust, data-driven assessment of farmland values by adjusting historical prices for inflation and applying technical analysis techniques to identify market dynamics. 

The objective is to assist policymakers, financial institutions, and stakeholders in making informed decisions.

Objectives:

1. Analyze long-term trends in Iowa farmland values using inflation-adjusted data.

2. Identify historical cycles, including peak-to-trough declines and recovery periods.

3. Apply technical analysis tools (logarithmic scale, RSI, moving averages, Fibonacci retracements/extensions, Elliott Wave Theory) to assess current market conditions.

4. Anticipate future price trend changes.

3. Data and Methodology

3.1 Data Sources

• 1920-1940: USDA NASS farm real estate values (www.nass.usda.gov) estimated for Iowa. includes buildings, inflating land-only values slightly

• 1941-2024: ISU Land Value Survey (1941-2024) (www.extension.iastate.edu/agdm, AgDM File C2-72)

• Inflation Data: The U.S. Bureau of Labor Statistics: BLS historical CPI-U (www.bls.gov/cpi, historical-cpi-u-2024.xlsx), with 2015 CPI = 237.3.

3.2 Inflation Adjustment

Using the U.S. Bureau of Labor Statistics BLS historical CPI-U, each historical data point was adjusted to reflect 2015 purchasing power. This process ensures that comparisons over time account for the eroding effects of inflation, enabling a clearer view of value changes.

3.3 Technical Analysis Methods

The study employs technical analysis techniques commonly used by market technicians to interpret price trends, momentum, and potential turning points in the inflation-adjusted data on Iowa farmland values.

3.3.1 Logarithmic Scale: 

A chart scale that displays percentage changes equally rather than absolute values. Used for long-term data to visualize exponential growth and reduce distortion.

Application: 

The land value price chart utilizes a logarithmic vertical scale, essential for analyzing long-term data with substantial growth, as it displays equal percentage changes with equal vertical distances.

Benefit: 

Enhances the visibility of growth trends over long periods and mitigates the distortion of exponential changes. 

3.3.2 Trendlines

A straight line connecting two or more price points on a chart. It helps identify the direction and strength of a trend. Upper and lower trendlines can signal resistance and support levels.

Application:

• Trendlines often act as support and resistance.

• A trendline was drawn connecting the highs of 1979 and 2013.

• The ascending nature of the trendline indicates a bullish trend in the price levels.

• The current price level is below the 2013 high.

3.3.3 Exponential Moving Average (EMA):

A moving average that places greater weight on recent data points. Used to identify trends by smoothing price data. A 2-year EMA in this report indicates short- to medium-term momentum in farmland values.

Application: 

A 2-period (2-year) Exponential Moving Average (EMA) is plotted on the price chart. 

• The EMA smooths price data to help identify the trend direction, giving more weight to recent years.

Benefit: 

Farmland values crossing the 2-year EMA signal a trend change.

Analysis: 

• Prices consistently above the EMA indicate an uptrend.

• The price has crossed below the 2-Year EMA eleven times in the past 124 years. 

• The current inflation-adjusted price (~$8,627 on the chart) is below the 2-Year EMA (~$8,745), indicating that a correction in farmland values may have begun.

• There have been three instances where declines in farmland values of 23.04%, 58.29% and 73.39% have followed past crosses below the 2-year Exponential Moving Average (EMA).

3.3.4 Relative Strength Index (RSI):

A momentum oscillator that measures the speed and change of price movements on a scale from 0 to 100. Readings above 70 are considered overbought, while readings below 30 are considered oversold.

Application:

Use the RSI indicator to detect overbought or oversold conditions.

Benefit: 

Helps pinpoint potential turning points in market sentiment, providing early warnings of market reversals.

Analysis:

A 14-period (14-Year) RSI is displayed in the lower panel. This momentum oscillator measures the speed and change of price movements on a scale of 0 to 100.

• Readings above 70 traditionally indicate potentially "overbought" conditions.

• Readings below 30 suggest potentially "oversold" conditions.

• Historical peaks near 92.75 (1979) and 88.85 (2013) preceded periods of declines in land values.

• Trendlines are drawn on both the upper price panel and lower RSI panel, displaying the bearish divergence between the price and RSI.

• The recent land value high of $ 9,313 (2023) corresponds with a lower RSI of 75.93, indicating bearish divergence, which suggests a potential weakening of the upward price trend and a possible price reversal to the downside.

Figure 1. Technical indicators applied to inflation-adjusted farmland values: Note the bearish divergence between the price and RSI indicated by the yellow trendlines.

3.3.5 Support and Resistance Analysis:

Support is a price level where demand is strong enough to prevent further decline. Resistance is a price level where selling pressure may prevent prices from rising further. These levels are used to predict price reversals or continuations.

Application: 

Examine how previous resistance levels have transitioned into support.

Benefit: 

Provides insight into the durability of price levels and helps forecast future market behaviour. 

Analysis: 

• These are historical price levels where buying (support) or selling (resistance) has tended to emerge. A key principle observed is that previous resistance, once broken, can become future support.

• The 1922 high of approximately $ 1,766 (inflation-adjusted) acted as support during the 1993 correction's low point.

• We anticipate that the resistance of the 1981 high, ~6384 (inflation-adjusted), will act as a magnet for values and could provide support in the future.

• A return to the inflation-adjusted 1922 value level of ~1766 should not be discounted.

3.3.6 Fibonacci Retracement

A tool used to identify potential reversal levels by dividing a price range into key ratios (23.6%, 38.2%, 50%, 61.8%, 78.6%). The Fibonacci retracement levels are commonly used to anticipate areas of support during price corrections.

Benefit:

Assists in projecting price corrections and extension targets based on historical price behaviour. 

Application: 

Apply Fibonacci tools to identify potential support and resistance levels.

• After a significant trend move, prices often "retrace" a portion of that move before continuing the trend. 

• Key Fibonacci retracement levels are calculated as percentages of the prior move: 23.6%, 38.2%, 50%, 61.8%, and 78.6%. 

• These levels can act as potential turning points or areas of consolidation during a correction.

Analysis: 

Retracement of the 1922-1933 Decline:

• The major correction saw inflation-adjusted values fall 59.29% from the peak near 1766 (1922) to the trough near 719 (1933).

Retracement of the 1979-1986 Decline:

• The major correction saw inflation-adjusted values fall 73.39% from the peak near 6384 (approximately 1979) to the trough near 1699 (approximately 1986).

Retracement of the 2013-2018 Decline:

• The major correction saw inflation-adjusted values decline 23.04% from the peak near $8,854 (2013) to the trough near $6,814 (2018).

Future inflation-adjusted value corrections in the range of 50 - 61.8% or even 78.6% should not be discounted.

3.3.7 Elliott Wave Analysis

A form of technical analysis that identifies cycles in financial markets. It posits that market prices move in a repeating pattern of five upward (impulsive) waves followed by three downward (corrective) waves.

Application: 

Identifies market cycles through a 5-wave impulsive structure followed by a 3-wave corrective structure.

Benefit: 

By recognizing the 5-wave impulse and 3-wave correction patterns, market participants can understand the overall direction of a market movement and capitalize on it. 

Analysis: 

Elliott Wave theory identifies market cycles through a 5-wave impulsive structure followed by a 3-wave corrective structure:

Primary Wave Structure

Wave 1:  0–1922:

• Impulsive Wave, culminating with the 1922 peak.

Wave 2:  1922–1933: 

• Corrective Wave (11 years), with the trough in 1933.

Wave 3:  1933–1979: 

• Impulsive wave (46 years), culminating in the 1979 peak.

Wave 4:  1979-1986:

• Corrective Wave (7 years),

Wave 5:  1986–current (2024): 

• New Impulsive wave (38 years). 

• When Wave 5 completes, a Corrective wave will follow, potentially leading to a significant decline.

• A return to the previous resistance level at the peak of Wave 3 (~6384) is anticipated.

• Wave 5 often corrects to the Wave 4 level (~1699), which aligns with the 1922 high and the 1986 low.

• A return to the inflation-adjusted level of ~1699 should not be discounted.

Figure 2. Elliott Wave Structure Applied to Iowa Farmland Values (1920–2024). Wave 5 may be approaching completion, implying the potential for a corrective wave.

3.3.8 Decline-Duration Analysis

Analysis of the length of time that farmland values or market prices experience a period of decline. This analysis can identify typical durations of market downturns, potential recovery timelines, and the impact of various factors on the length of declines.

Application:

Evaluate the duration from peak to trough and subsequent peaks.

Benefit: 

Offers a measure of market cyclicality and recovery speed, essential for long-term forecasting.

Analysis:

The data reveals three significant declines in farmland values, each associated with broader economic downturns:

Table 1: Historical Declines in Farmland Values

4. Conclusion

The analysis indicates that Iowa farmland values have grown significantly over the long term when adjusted for inflation, despite experiencing cyclical corrections. 

Technical indicators, such as the RSI and moving averages, provide valuable signals for market turning points, while historical support and resistance levels offer insight into the resilience of land values.

Key Findings:

• Integrated Analysis: 

  • The combination of inflation-adjusted data and advanced technical analysis provides a nuanced understanding of historical land values.

• Cyclical Trends: 

  • Technical tools such as RSI, moving averages, and Elliott Wave analysis reveal distinct cyclical patterns that align with broader economic cycles.

• Support and Resistance: 

  • Historical support and resistance levels, particularly those aligning with Fibonacci analysis, are crucial in forecasting future price behaviour.

Implications:

• For Policymakers: 

  • Monitoring indicators and potential catalysts can help in designing supportive policies that stabilize the farmland market during periods of downturn.

• For Investors and Lenders: 

  • Understanding the timing of cycles and technical signals can enhance risk management and investment decisions.

• For Farmers: 

  • Awareness of market cycles and trends can inform strategies related to land sales, refinancing, or expansion.
    

Disclaimer: This analysis is based on historical data and technical interpretations. Market conditions are dynamic and subject to change. Past performance is not indicative of future results. This report does not constitute investment advice.

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Trent Klarenbach

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Nothing written, expressed, or implied here should be considered investment advice or an admonition to buy, sell, or trade any security or financial instrument. As always, do your own due diligence.