According to Cognitive Market Research, the Global Pre-owned Luxury Watches market size is $26,832.60 Million in 2024 and it is forecasted to reach $43,653.90 Million by 2031. Pre-owned Luxury Watches Industry's Compound Annual Growth Rate will be 7.20% from 2024 to 2031. Market Dynamics of the

Pre- owned Luxury Watches Market

Market Drivers of the

Pre owned Luxury Watches Market

Growing recognition of high-end timepieces as both status symbols and enduring investments is boosting the demand for pre-owned luxury watches

The concept of luxury has been changing dramatically across time and culture. Earlier, luxury was connected with things like wines, champagne, designer clothes and sports cars. These days, people have become richer and luxury is a blurred generation that is no longer the preserve of the elite. People are having much more disposable income in comparison to earlier generations, resulting in a tendency brands like apple mobile, boat watches. Luxury watches have gained popularity over the years with Swiss watches continuing to be the heart of the industry.

From August 2018 to January 2023, average prices in the second-hand market for top models from the three largest luxury brands— Rolex, Patek Philippe, and Audemars Piguet—rose at an annual rate of 20%, despite broader market downturns during the pandemic, compared with an annual rate of 8% for the S&P 500 index. Wealthy investors increasingly seek alternative investments to diversify their portfolios and to hedge against inflation. For these and other investors, luxury watches stand out as a class of alternative assets because of the strong demand for them and because they have generally delivered strong price performance in the market over the past five to ten years. Buyers regard the category as a stable investment built on reputable brands and supported by a consumer base of high-net-worth individuals. In the ten-year period from 2013 to 2022, watches outperformed collectible assets such as jewellery, handbags, wine, art, and furniture, growing in value at an average annual rate of 7%—and by 27% from 2020 to 2022—according to indices that track these categories. Classic buyers typically invest in traditional financial assets and appreciate durable, credible products. They purchase across price ranges and seek classic or timeless watches with a strong brand heritage or a distinctive design. Whereas, there are customers which can be categorized into two different segments which include, luxury watch hobbyist and collector/investor. Hobbyist buyers prefer technically complex watches, with a strong brand heritage in the super-luxury category, where watch value is generally expected to increase over time. Moderately frequent buyers, hobbyists (77% of whom are male) tend to be status-conscious and successful. Much of the pleasure they find in purchasing a second-hand watch is in the hunt for a special item. On the other hand, members of this buyer segment are the most active buyer group, on average, favouring ultra-luxury watches at a higher price point than other segments prefer. They represent 44% of watch buyers and claim a 58% share of the market by value. This segment is highly engaged with the secondary market, with nearly three-quarters having bought a second-hand piece in the past 24 months.

Therefore, one major reason that the secondary market has grown is clearly that consumers seeking investment opportunities are gravitating to it. Gen Z and younger millennial buyers said that they had increased their spending on luxury watches during the previous 24 months, citing increased ease of buying and selling and more investment opportunities as their top reasons.

Rising second-hand luxury watches consumption is gaining popularity, thereby, driving the market growth

Global sales of second-hand luxury products are steadily increasing. While there are more people than ever interested in owning a watch, luxury brands, which include the big four: Patek Philippe, Rolex, Audemars Piguet, and Richard Mille continue to produce limited inventory every year to ensure exclusivity and quality. Then there is the general growth in the second-hand luxury market. Since the pandemic took hold, consumers have begun investing in long-lasting, quality items, with luxury sales set to beat pre-COVID numbers this year. On Rebag, most watches sell within a few...

Learn about the shrinking demand for watches worldwide and the forecasted downward consumption trend over the next six years.

Since mid 2022, market prices of a selected group of most traded luxury watches have consistently declined. As of ***********, the average price of a luxury watch was worth ****** U.S. dollars.

This index tracks performance of Rolex's most coveted sports models including Submariner, GMT-Master, Daytona, Explorer, Sea-Dweller, and Yacht-Master. Measures price appreciation, market demand, and investment returns across metal variants. Includes vintage references and modern iterations. Key indicator for luxury sports watch market health, collector sentiment, and Rolex brand strength. Essential benchmark for crown jewel of sports watch collecting with consistent appreciation and strong liquidity.

Graph and download economic data for Producer Price Index by Industry: Jewelry and Silverware Manufacturing: Lapidary Work, Except for Watch Jewels (PCU3399103399107) from Sep 2016 to Mar 2021 about timepiece, utensils, jewelry, manufacturing, PPI, industry, price index, indexes, price, and USA.

This index measures premium pricing and appreciation of stainless steel luxury sports watches including Patek Philippe Nautilus 5711, AP Royal Oak 15202, Rolex sports models, and VC Overseas. It tracks unprecedented demand for steel over precious metals in luxury segment. Use this as a key indicator of modern collecting preferences, accessibility vs. exclusivity balance, and supply-demand dynamics. Essential benchmark for contemporary luxury sports watch investment performance and market desirability shifts.

United States - Producer Price Index by Industry: Other Measuring and Controlling Device Manufacturing: Watches, Clocks, and Parts was 131.27700 Index Dec 2003=100 in August of 2022, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Industry: Other Measuring and Controlling Device Manufacturing: Watches, Clocks, and Parts reached a record high of 131.27700 in August of 2022 and a record low of 100.00000 in December of 2003. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Industry: Other Measuring and Controlling Device Manufacturing: Watches, Clocks, and Parts - last updated from the United States Federal Reserve on July of 2025.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Watches of Switzerland Group (WOSG) Soaring or Drowning in the Luxury Market?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This index monitors dual-time and world-time complications from Rolex GMT-Master, Patek Philippe World Time, Vacheron Constantin Overseas Dual Time, JLC Master Geographic, and Tudor Black Bay GMT. Tracks appreciation of travel-focused complications essential for global business and aviation. Measures collector demand for practical luxury complications. Use this as a key indicator for international traveler preferences, business executive watch choices, and sophisticated complication market performance in global economy.

United States PPI: Mfg: Misc: OM: JS: PP: Lapidary Work excl for Watch Jewels data was reported at 104.100 Sep2016=100 in Mar 2021. This stayed constant from the previous number of 104.100 Sep2016=100 for Feb 2021. United States PPI: Mfg: Misc: OM: JS: PP: Lapidary Work excl for Watch Jewels data is updated monthly, averaging 100.550 Sep2016=100 from Sep 2016 (Median) to Mar 2021, with 52 observations. The data reached an all-time high of 104.100 Sep2016=100 in Mar 2021 and a record low of 99.800 Sep2016=100 in Jul 2018. United States PPI: Mfg: Misc: OM: JS: PP: Lapidary Work excl for Watch Jewels data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.I: Producer Price Index: by Industry: Manufacturing: Miscellaneous.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

First Watch (FWRG) Ready for Breakfast?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Denmark Retail Trade Index: Watches and Clocks data was reported at 480.000 2000=100 in Dec 2008. This records an increase from the previous number of 202.000 2000=100 for Nov 2008. Denmark Retail Trade Index: Watches and Clocks data is updated monthly, averaging 112.500 2000=100 from Jan 2000 (Median) to Dec 2008, with 108 observations. The data reached an all-time high of 499.000 2000=100 in Dec 2007 and a record low of 79.000 2000=100 in Feb 2001. Denmark Retail Trade Index: Watches and Clocks data remains active status in CEIC and is reported by Statistics Denmark. The data is categorized under Global Database’s Denmark – Table DK.H007: Retail Trade Index: by Industry: 2000=100.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

First Watch's Future (FWRG)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

April 9, 2025, saw the largest one-day gain in the history of the Dow Jones Industrial Average (DJIA), follwing Trump's announcement of 90-day delay in the introduction of tariffs imposed on imports from all countries. The second-largest one-day gain occurred on March 24, 2020, with the index increasing ******** points. This occurred approximately two weeks after the largest one-day point loss occurred on March 9, 2020, which was triggered by the growing panic about the coronavirus outbreak worldwide. Index fluctuations The DJIA is an index of ** large companies traded on the New York Stock Exchange. It is one of the numbers that financial analysts watch closely, using it as a bellwether for the United States economy. Seeing when these large gains occur, as well as the largest one-day point losses, gives insight to why these fluctuations may occur. The gains in 2009 are likely adjustments after major losses during the Financial Crisis, but those in 2018 are probably signs of high market volatility. Other leading financial indicators While the DJIA is closely watched, it only gives insight on the performance of thirty leading U.S. companies. An index like the S&P 500, tracking *** companies, can give a more comprehensive overview of the United States economy. Even so, this only reflects investment. Other parts of the economy, such as consumer spending or unemployment rate are not well reflected in stock market indices.

Between the second half of 2020 and the first of 2021 online sales of jewelry and watches grew by about ** percent compared to the previous equivalent period. Average receipt was the indicator with the smallest growth rate during the period.

Jewellery and watch wholesalers in Canada have experienced tepid growth in recent years because of a tough macroeconomic environment and soaring prices for precious metals. The COVID-19 pandemic and its associated economic volatility, including recovery, sent gold prices skyrocketing because of its status as a safe-haven asset. Gold is the most popular metal for fine jewellery and is even used to plate costume jewellery. This boosted prices at jewellery stores, which cut into demand from Canadians who were also dealing with sharp inflation from the pandemic-related recovery, which held per capita disposable income down. These two trends mostly offset each other and kept revenue mostly flat. Overall, revenue inched upward at a CAGR of 0.3% over the past five years to reach $4.2 billion in 2024. This includes a forecast 6.3% increase in 2024 alone as inflation began to slow, while prices remained high, boosting demand without a decrease in price. Wholesalers were able to pass on costs to retailers, which kept profit stagnant. The macroeconomic outlook over the next five years is mostly sunny, which will aid jewellery and watch wholesalers and accelerate revenue growth. Consumer confidence, consumer spending and per capita disposable income are all forecast to be on the rise, while inflation has been tamped down and interest rates are expected to remain lower, providing for a better environment for Canadians to make discretionary purchases, including fine jewellery. Wholesalers will also benefit from stabilized, albeit elevated, prices of gold, platinum and silver, preventing prices from falling too far. However, the specter of tariffs from the United States looms to buck these macroeconomic trends and also break supply chains, making jewellery and watches less available, as the country's imports represents a significant share of domestic demand. Revenue is forecast to climb at a CAGR of 2.6% over the next five years to reach an estimated $4.8 billion in 2029.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

First Watch (FWRG) Stock: A Recipe for Growth?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This index tracks pre-1980s Rolex models including early Submariner 6204/6536/6538, GMT-Master 6542/1675, Daytona 6239/Paul Newman, Explorer 1016, Milgauss 6541, and Sea-Dweller 1665. It measures vintage appreciation, historical significance value, and collector nostalgia for crown's golden era. Use this a critical indicator for vintage sports watch investment, historical model performance, and Rolex heritage appreciation. Essential benchmark for classic crown collecting and vintage luxury sports watch market strength.

United States PPI: Mfg: CE: NM: OM: PP: Watches, Clocks & Parts data was reported at 131.277 Dec2003=100 in Aug 2022. This records an increase from the previous number of 127.147 Dec2003=100 for Jul 2022. United States PPI: Mfg: CE: NM: OM: PP: Watches, Clocks & Parts data is updated monthly, averaging 118.600 Dec2003=100 from Dec 2003 (Median) to Aug 2022, with 225 observations. The data reached an all-time high of 131.277 Dec2003=100 in Aug 2022 and a record low of 100.000 Dec2003=100 in Dec 2003. United States PPI: Mfg: CE: NM: OM: PP: Watches, Clocks & Parts data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.I: Producer Price Index: by Industry: Manufacturing: Computer and Electronic Products.

Denmark Retail Trade Index: Watches and Jewellery data was reported at 103.000 2021=100 in Feb 2025. This records an increase from the previous number of 101.000 2021=100 for Jan 2025. Denmark Retail Trade Index: Watches and Jewellery data is updated monthly, averaging 75.000 2021=100 from Jan 2000 (Median) to Feb 2025, with 302 observations. The data reached an all-time high of 249.000 2021=100 in Dec 2024 and a record low of 19.000 2021=100 in Jan 2021. Denmark Retail Trade Index: Watches and Jewellery data remains active status in CEIC and is reported by Statistics Denmark. The data is categorized under Global Database’s Denmark – Table DK.H005: Retail Trade Index: by Industry: 2021=100.