The 2017 European Fine Art Foundation Art Market Report reported auction sales in China in 2011 had overtaken US sales, making China the largest art market in the world. While holding art as an alternative investment has become attractive to art collectors and investors, research has focused on Western countries, such as Australia, European countries, and the U.S. Due to language, culture, and data limitations, few papers focus on the Chinese art market. As a result, the investors and collectors still lack the comprehensive investment knowledge for the Chinese art market.
This research examines the investment returns and price determinants of 171,941 Chinese artworks from 580 artists over the period of 2000-2017. The study proposes and estimates a Chinese art price index through a hedonic and repeat sales regression, and further designed art price indices by movements and types and found that the real rate of return varies among the different movement and type.
48
The study also considered the price determinants of Chinese art, and the results showed that consistent with other hedonic pricing models for art markets, the capture of the artists’
characteristics, and works’ characteristics, and sales’ characteristics contribute to the realized market price. Furthermore, the study tracked the price determinants of a certain hedonic variable in the long-run movement, finding the price effect of a certain attribute changed across the various movements or types.
This study provides more evidence on the masterpiece effect in the Chinese art market. The results indicate that the masterpiece effect exists in the general art market, while the masterpieces underperform in the repeat sales market.
The study analyzes the auction house effect between different art market, areas with different ownership or geographical location. In general, the Chinese art market violates the auction house effect. The results showed that paintings sold at Sotheby’s and Christie's and State-owned auction houses fetch higher prices, and that investors enjoy a significantly positive return when they bought the artwork in Hong Kong and then sold in Beijing. In addition, short-horizon art investors tend to receive high premium across the different art market, indicating that short-horizon investors focus on arbitrage.
49
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List of Tables
Table 1 Descriptive Statistics Hedonic Variables
Table 1 summarized descriptive statistics for hedonic variables. The hedonic dataset consists of 171941 lots by 580 Chinese artists from 2000-2017. Table 1 reports the results of number of observations, turnover (CNY) and mean price (CNY). “CHR” refers to Christie’s (Hong Kong; London; New York) and “Soth” is expressed as Sotheby’s (Hong Kong; London; New York).
Table 1 Descriptive Statistics Variables
Variables N Turnover Mean
(000,000) (000)
Artist characteristics Art movements
Ancient 20,847 25,658 1,231
Modern 115,038 111,132 966
Contemporary 36,056 40,816 1,132
Art types
Non-oils 164,714 147,434 895
Oils 7,227 30,172 4,175
Artist attributes
Deceased 133,717 140,704 1,052
Mentorship 82,048 77,964 950
Work characteristics Authenticity variables
Celebrity 14,318 21,112 1,475
Dated 90,837 117,333 1,292
Exhibition 7,734 39,286 5,080
Literature 10,808 13,794 1,276
Provenance 26,946 56,274 2,088
Signed 151,953 162,455 1,069
53
Table 1 Descriptive Statistics Variables
Variables N Turnover Mean
(000,000) (000)
Mounting variables
Albums 3,470 9,147 2,636
Banner paintings 974 798 819
Canvas 7,225 30,170 4,176
Drawing axis 927 280 302
Fans 9,545 2,077 218
Frame 7,014 9,390 1,339
Handscroll 2,870 7,128 2,483
Jing pian 4,647 3,879 835
Jing xin 53,854 39,093 726
Rice painting 520 179 343
Screens 1,712 4,656 2,720
Vertical drawing 74,943 65,646 876
Sale characteristics
Month variables
January 5,383 1,408 262
February 412 174 422
March 8,731 1,896 217
April 11,452 9,808 856
May 22,004 32,564 1,480
June 29,467 34,420 1,168
July 12,036 7,528 625
August 3,311 564 170
September 8,144 2,489 306
October 9,667 12,383 1,281
November 26,987 32,950 1,221
December 34,347 41,422 1,206
54
Table 1 Descriptive Statistics Variables
Variables N Turnover Mean
(000,000) (000)
China mainland auction houses
Beijing Poly 21,531 32,020 1,487
China Guardian 39,028 35,498 910
Council 8,829 12,973 1,469
Duoyunxuan 8,168 5,280 646
Hanhai 18,919 11,433 604
Holly's International 5,446 4,076 748
Rong Bao Zhai-BJ 12,533 6,403 511
Sungari International 8,422 5,895 700
Tianheng-SH 2,009 3,485 1,735
XiLing Yinshe Auction 7,813 7,142 914
Non-China owned auction houses
CHR-LONDON 113 153 1,355
CHR-HK 9,519 19,402 2,038
CHR-NY 155 69 448
SOTH-HK 6,305 15,586 2,472
SOTH-LONDON 118 111 940
SOTH-NY 819 936 1,142
Year variables
2000 1,941 439 226
2001 2,267 466 206
2002 3,798 648 171
2003 4,893 997 204
2004 11,651 3,936 338
2005 12,935 7,192 556
2006 10,677 5,420 508
2007 9,169 7,076 772
2008 7,599 5,339 703
2009 11,919 8,251 692
2010 15,781 21,692 1,375
2011 16,018 31,641 1,975
2012 11,406 14,143 1,240
2013 12,095 16,574 1,370
2014 13,029 14,640 1,124
2015 9,149 11,247 1,229
2016 8,485 11,732 1,383
2017 9,129 16,173 1,772
55 Table 2 Descriptive Statistics Repeat sales Variables
Table 2 summarizes the descriptive statistics of 4,750 repeat records used in the repeat sales regression. The repeat sales records are classified into three categories:
Art movement, Art types, and Horizon of the investment. Holding Period is the time between two adjacent resales, and Sale Price is the price of sale. In horizon of the investment category, holding periods from one to two semi-annual are classified as short horizon (Short), those from three to six semi-annual are categorized as the medium horizon (Medium), and those over six semi-annual are categorized as the long horizon (Long).
Table 2 Descriptive Statistics of Repeat Sales Variables
Movement category Type category Horizon category Ancient Modern Contemporary Non-oils Oils Short Medium Long
Number of observations 409 1,080 3,261 4,332 418 1,167 1,726 1,857
Holding Period (Semi-annual) 6 7 7 7 8 2 4 12
Purchase price (1000 CNY) 1,607 1,472 1,336 1,196 3,400 873 1,455 1,655 Resales price (1000 CNY) 2,488 2,113 2,308 2,009 5,079 1,083 1,832 3,446
0<Purchase<=10,000 12 8 14 34 - 10 11 13
10,000<Purchase<=50,000 54 128 246 425 3 111 153 164
50,000<Purchase<=200,000 104 290 861 1,225 30 326 445 484
200,000<Purchase<=1000,000 116 330 1,255 1,596 105 456 576 669
Purchase>1000,000 123 324 885 1,052 280 264 541 527
56 Table 3 Descriptive Statistics 67 Repeat sales Records
Table 3 summarizes the descriptive statistics for 67 repeat sales transactions. The repeat price index is defined on a semi-annual basis, but the holding period of 67 repeat sales records is less than half a year, which cannot construct the time dummy variable for them. Thus, the current research excludes 67 repeat sales records from the regression. Holding Period is the time between two adjacent resales.
Table 3 Descriptive Statistics 67 Repeat sales Records
Ancient Modern Contemporary Total
Number of observations 5 54 8 67
Average Holding Time (Monthly) 3.46 3.35 4.58 3.5
Average holding period return (Monthly) 0.72 0.15 0.01 0.18
0<Purchase<=10,000 - 1 1 2
10,000<purchase<=50,000 1 9 2 12
50,000<purchase<=200,000 2 18 2 22
200,000<purchase<=1000,000 2 17 3 21
purchase>1000,000 - 9 - 9
57 Table 4 Baseline Hedonic Regression Results
Table 4 reports the parameter estimates of the hedonic variables for the baseline hedonic model. The dependent variable is the natural log of the real price in CNY.
The Hedonic regression yields respectable R2 of 60.39 per cent and almost coefficients are statistically significant. For each variable, Table 4 reports the estimated hedonic coefficient, the standard error (SE), T value, P value, and the price impact (i.e., the exponent of the estimated coefficient minus one). “CHR” refers to Christie’s (Hong Kong; London; New York) and “Soth” is expressed as Sotheby’s (Hong Kong; London; New York).
Table 4 Baseline Hedonic Regression
Coefficient SE t Value Pr > |t| price Index (%)
Year dummies [Included]
Artist characteristics
Artist dummies [Included]
Work characteristics Authenticity dummies
Celebrity 0.300 0.010 30.710 <.0001 35.051
Dated 0.335 0.006 56.270 <.0001 39.777
Exhibition 0.912 0.013 69.150 <.0001 149.009
Literature 0.523 0.011 47.910 <.0001 68.720
Provenance 0.267 0.008 35.130 <.0001 30.607
Signed 0.237 0.010 24.070 <.0001 26.717
Mounting dummies
Albums 0.662 0.026 25.100 <.0001 93.841
Banner paintings 0.153 0.039 3.890 0.0001 16.496
Canvas 0.348 0.326 1.070 0.2858 41.683
Drawing axis 0.233 0.042 5.550 <.0001 26.244
Fans -0.647 0.022 -29.540 <.0001 -47.641
Frame -0.087 0.023 -3.780 0.0002 -8.309
Handscroll 0.584 0.028 20.850 <.0001 79.406
Jing pian -0.299 0.025 -12.040 <.0001 -25.859
Jing xin -0.104 0.019 -5.460 <.0001 -9.922
Rice paintings -0.602 0.052 -11.520 <.0001 -45.219
Screens 0.198 0.033 6.080 <.0001 21.927
Vertical drawing -0.082 0.019 -4.310 <.0001 -7.849
58
Table 4 Baseline Hedonic Regression
Coefficient SE t Value Pr > |t| price Index (%)
Size variables
size 5.57E-05 4.15E-07 134.100 <.0001 0.006
size square -3.77E-11 3.71E-13 -101.520 <.0001 -3.7673E-09
Sale characteristics Month dummies
February -0.398 0.056 -7.160 <.0001 -32.863
March -0.290 0.020 -14.820 <.0001 -25.199
April -0.077 0.019 -4.070 <.0001 -7.398
May 0.582 0.017 33.890 <.0001 78.994
June 0.477 0.017 28.620 <.0001 61.127
July 0.281 0.019 15.140 <.0001 32.502
August -0.288 0.025 -11.750 <.0001 -25.058
September -0.208 0.020 -10.470 <.0001 -18.778
October 0.122 0.020 6.250 <.0001 13.023
November 0.527 0.017 31.270 <.0001 69.437
December 0.486 0.017 29.440 <.0001 62.569
China mainland auction houses dummies
Beijing Poly -0.312 0.011 -28.020 <.0001 -26.830
China Guardian 0.137 0.010 14.120 <.0001 14.625
Council 0.241 0.015 16.590 <.0001 27.258
Duoyunxuan 0.255 0.015 16.930 <.0001 29.059
Hanhai -0.311 0.011 -27.320 <.0001 -26.710
Holly's International 0.145 0.018 8.260 <.0001 15.604
Rong Bao Zhai-BJ -0.034 0.013 -2.650 0.0082 -3.324
Sungari International 0.294 0.014 20.310 <.0001 34.134
Tianheng-SH 0.425 0.026 16.310 <.0001 52.962
XiLing Yinshe Auction 0.451 0.016 28.120 <.0001 57.021
Non-China owned auction houses dummies
CHR-LONDON 0.229 0.102 2.230 0.0255 25.680
CHR-HK 0.330 0.014 23.120 <.0001 39.123
CHR-NY 0.626 0.088 7.130 <.0001 87.028
SOTH-HK 1.005 0.018 56.610 <.0001 173.243
SOTH-LONDON 0.691 0.101 6.870 <.0001 99.485
SOTH-NY 1.193 0.040 29.820 <.0001 229.646
Observation 171941
R 0.6035
59
Table 5 Comparison &Contrast Results of Chinese art across the various types and movements
Table 5 reports the Contrast Results of Chinese art across the various movement. For each variable, Table 5 reports the coefficient, standard error, t value, P value, and the price impact (i.e., the exponent of the estimated coefficient minus one). Panel A of Table 5 constructs the intersection between ancient period and hedonic attributes and reports a comparison and contrasting results of Chinese art between ancient and modern. Panel B of Table 5 constructs the intersection between modern and hedonic attributes and reports a comparison and contrast the results of Chinese art between modern and contemporary. All estimations contain the full set of hedonic artwork characteristics, suppressed due to space constrains.
Table 5 Comparison & Contrast Results of Chinese art in art movements
Panel A Panel B
Ancient - Modern Modern-Contemporary
Coefficient (Ancient * Hedonic
attributes)
SE t Value Pr > |t|
Price Coefficient (Modern*Hedonic
attributes)
SE t Value Pr > |t|
Price Impact
(%)
Impact (%) Artist characteristics
Deceased - - - - - 0.554 0.039 14.110 <.0001 73.933
Mentorship -0.039 0.023 -1.680 0.092 -3.837 0.057 0.016 3.650 0.000 5.907
Work characteristics Authenticity dummies
Dated 0.200 0.021 9.370 <.0001 22.102 -0.140 0.016 -8.630 <.0001 -13.076
Signed -0.093 0.037 -2.530 0.011 -8.909 0.339 0.023 14.470 <.0001 40.322
Provenance 0.030 0.028 1.070 0.285 3.006 0.219 0.020 11.170 <.0001 24.467
Celebrity 0.048 0.034 1.400 0.160 4.958 0.241 0.046 5.240 <.0001 27.227
Literature 0.750 0.053 14.250 <.0001 111.664 0.113 0.029 3.970 <.0001 11.988
Exhibition 0.619 0.077 8.000 <.0001 85.679 0.815 0.032 25.310 <.0001 126.012
Mounting dummies
Vertical drawing -0.048 0.064 -0.750 0.455 -4.645 0.457 0.050 9.180 <.0001 57.904
Jing Xin -0.421 0.069 -6.090 <.0001 -34.370 0.183 0.049 3.760 0.000 20.120
Frame -0.162 0.118 -1.370 0.170 -14.923 0.138 0.060 2.310 0.021 14.760
Albums 0.052 0.078 0.660 0.509 5.302 0.423 0.092 4.580 <.0001 52.675
Handscroll 0.187 0.082 2.270 0.023 20.512 -0.021 0.105 -0.200 0.840 -2.094
Jing Pian -0.125 0.115 -1.090 0.277 -11.763 0.112 0.064 1.760 0.079 11.892
Screens -0.779 0.100 -7.770 <.0001 -54.127 0.120 0.107 1.120 0.262 12.787
Rice Paintings 0.259 1.331 0.190 0.846 29.545 -0.081 0.139 -0.580 0.561 -7.777
Banner paintings -0.368 0.161 -2.280 0.022 -30.759 0.123 0.108 1.140 0.255 13.121
Drawing axis 0.684 0.129 5.290 <.0001 98.163 0.243 0.231 1.050 0.294 27.466
Fan covers 0.325 0.070 4.640 <.0001 38.371 -0.059 0.081 -0.730 0.468 -5.716
Canvas - - - - - -0.722 0.069 -10.470 <.0001 -51.399
Number of observations 135,803 148,853
R-Square 0.3733 0.4395
60 Table 6 hedonic art price index
Table 6 depicts the art price index of the hedonic regression for Chinese art from 2000 to 2017. Based on the coefficients of the time dummies and the estimated variance of residuals in each period, this study respectively constructed both an uncorrected art price index and a price index that corrects for log transformation bias.
Table 6 Hedonic art price index of Chinese art
Full Sample Ancient Modern Contemporary Non-oils Oils
Year
Uncorrected art price
index
Corrected price index
Uncorrected art price
index
Corrected price index
Uncorrected art price
index
Corrected price index
Uncorrected art price
index
Corrected price index
Uncorrected art price
index
Corrected price index
Uncorrected art price
index
Corrected price index
2000 100 100 100 100 100 100 100 100 100 100 100 100
2001 119.2 119.25 104.39 105.54 107.51 107.53 144.81 144.87 118.5 118.55 167.51 167.8 2002 93 93.03 60.99 61.69 81.78 81.79 97.38 97.69 93.34 93.4 125.04 123.39 2003 144.55 144.67 112.68 114.49 126.92 126.92 125 125.71 143.83 143.96 279.61 280.75 2004 198.63 198.62 124.42 126.4 179.97 179.99 153.19 153.5 199.49 199.63 328.17 321.93 2005 341.47 341.03 218.21 221.63 300.64 300.55 305.58 304.73 343.71 344.09 580.73 563.95 2006 304.11 304 168.35 169.86 234.85 234.92 302.7 304.44 299.04 299.02 738.34 744.67 2007 292.84 292.69 160.2 161.93 234.34 234.39 325.51 327.23 277.9 277.88 987.15 995.27 2008 280.62 280.64 152.14 154.46 218.06 218.14 305.39 306.8 268.44 268.59 920.58 925.68 2009 282.63 280.12 144.7 143.43 237.37 234.55 284.03 283.18 283.97 281.44 602 605.26 2010 445.14 439.9 211.31 209.4 363.94 356.69 412.55 413.08 448.61 443.22 880.4 888.94 2011 689.71 687.78 269.64 272.11 591.55 588.47 694.08 697.47 707.12 705.15 1016.56 1026.84 2012 554.08 552.25 187.53 188.49 424.44 422.36 651.63 653.51 563.42 561.52 891.49 901.35 2013 626.7 621.97 225.96 225.98 442.92 437.58 684.82 684.72 636.76 631.84 1022.62 1033.5 2014 548.51 541.45 157.42 155.23 369.35 361.37 586.82 585.44 557.17 549.89 923.43 931.37 2015 393.9 388.22 125.03 123.59 266.81 260.3 427.01 426.06 395.9 390.09 809.71 815.6 2016 349.03 339.91 108.68 103.25 257.66 247.54 316.74 313.02 351.6 342.35 812.59 812.19 2017 462.49 446.78 167.82 152.3 364.06 345.59 480.67 475.08 466.04 450.01 849.01 852.68
61
Figure 1 presents the evolution of the corrected hedonic art price indices from 2000 to 2017.
The price levels in 2000 are standardized to 100. The art price indices in the baseline, art movements, and art types always move in the same direction, and show that the Chinese art market experienced two bull periods.
0 200 400 600 800 1000 1200
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Corrected hedonic art price index
Full Sample Anicent Modern Contemporary Non-oils Oils
62 Table 7 Investment returns of Chinese art by hedonic regression
Table 7 reports the investment returns of Chinese art by using hedonic regression. Panel A of Table 7 presents the annualized (i.e., geometric average) returns and standard deviations (S.D.) over the periods 2000-2017 for the baseline art price indices detailed in Table 4. Panel B shows the return estimates for the different types and movements considered in current research.
Table 7 Investment returns of Chinese art by hedonic regression Real Returns
2000-2017
N Mean SD
Panel A Hedonic baseline indices
Uncorrected art price index 171941 13.54 32.16
Corrected art price index 171941 13.16 32.41
Panel B Per artistic type and per movement
Non-oils 164,714 13.43 32.72
Oils 7,227 19.82 41.34
Ancient 20,847 8.16 37.43
Modern 115,038 12.30 33.55
Contemporary 36,056 14.81 36.93
63 Table 8 Masterpiece Effect
The art dealers commonly advise investors to buy the most expensive artwork, on the presumption that the masterpieces will outperform the general art market (J. E.
Pesando, 1993). In other words, the most expensive artwork (denoted as masterpieces) has a higher return than middle-priced and low-priced artwork. The current research not only investigates the masterpiece effect in the general art market but also studies this effect in the repeat sales market. Panel A of Table 8 reports the masterpiece effect by using quantile regression. Panel B of Table 8 shows the results of masterpiece effect by using the adjacent period hedonic regression. Panel C of Table 8 reports the results of the masterpiece effect by using repeat sales regression.
Table 8 Masterpiece Effect Panel A Quantile Regression
Q5 Q25 Q50 Q75 Q95
Mean SD Mean SD Mean SD Mean SD Mean SD
Full Sample 0.092 0.375 0.112 0.352 0.126 0.335 0.143 0.327 0.154 0.338
Ancient 0.066 0.404 0.088 0.399 0.107 0.407 0.124 0.443 0.109 0.402
Modern 0.096 0.365 0.118 0.345 0.131 0.334 0.149 0.333 0.162 0.357
Contemporary 0.132 0.465 0.137 0.406 0.141 0.350 0.165 0.348 0.202 0.401
Panel B Adjacent-Period Hedonic Regression
Q5 Q25 Q50 Q75 Q95
Mean SD Mean SD Mean SD Mean SD Mean SD
Full Sample 0.104 0.339 0.118 0.334 0.140 0.354 0.151 0.362 0.162 0.366
Ancient 0.053 0.359 0.093 0.404 0.093 0.400 0.092 0.432 0.063 0.476
Modern 0.114 0.326 0.118 0.334 0.130 0.348 0.154 0.378 0.169 0.419
Contemporary 0.156 0.402 0.144 0.371 0.164 0.352 0.208 0.350 0.264 0.434
Panel C Repeat sales regression
Full Sample Ancient Modern Contemporary Non-oil Oil Short Medium Long
γ -0.002 -0.0028 -0.0001 -0.004 -0.0006 0.0075 -0.0052 -0.0102 0.0002
(-2.4824) (-0.8003) (-0.1261) (-2.5456) (-0.7271) (2.7817) (-1.39) (-5.5359) (0.2087)
64 Table 9 Auction house effect
The relative ease of cross-border movement in the art market, the investors may cause cross-market excessive speculation. The current research identifies the circulation of the art between different market according to the data of sale venue in the repeat sales to investigates the auction house effect in the Chinese art market. Panel A of Table 9 shows the result of the auction house effect between the Chinese art market and foreign art market. Panel B of Table 9 shows the result of the auction house effect between the state-owned auction house and “non”
state-owned auction house. Panel C of Table 9 shows the result of the auction house effect between the onshore and offshore art markets.
Table 9 Auction house effect
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Buy-sale Full sample Ancient Modern Contemporary Non-oils Oils Short horizon
Medium horizon
Long horizon Panel A Chinese auction house – Foreign auction house
Foreign auction house - Chinese
auction house (F-C) 0.032 0.013 0.029 0.036 0.029 0.021 0.309 0.062 0.026
(3.97) (0.304) (3.241) (1.897) (3.201) (1.156) (4.381) (2.702) (2.938)
Chinese auction house-
Foreign auction house (C-F) 0.018 0.39 0.031 0.008 0.029 -0.0002 0.363 0.126 0.002
(1.822) (0.882) (2.696) (0.494) (2.321) (-0.014) (4.140) (4.108) (0.183)
Chinese auction house - Chinese
auction house (C-C) 0.010 -0.029 0.012 0.012 0.010 -0.004 0.070 0.027 0.006
(1.573) (-0.796) (1.746) (1.063) (1.356) (-0.322) (1.692) (1.559) (0.889)
Panel B State owned auction house - non-state-owned auction house State owned auction house -
Non-state-owned auction house (S-N)
0.015 0.015 0.016 0.001 0.014 0.008 0.119 0.048 0.01
(3.441) (0.601) (3.424) (0.138) (3.125) (0.577) (2.999) (3.892) (2.091)
Non-state-owned auction house -
State owned auction house (N-S) 0.014 0.01 0.013 0.016 0.011 0.041 0.043 0.046 0.01
(3.553) (0.453) (3.002) (1.756) (2.548) (2.921) (1.198) (4.293) (2.121)
Non-state-owned auction house- Non-state-owned auction house (N-N)
0.01 0.027 0.007 0.007 0.009 0.008 0.064 0.022 0.007
(3.087) (1.492) (1.922) (1.086) (2.821) (0.858) (2.508) (2.769) (1.890)
Panel C onshore art market and offshore art market
Beijing-Hong Kong (B-H) 0.007 -0.050 0.010 -0.007 0.009 0.007 0.220 0.050 -0.003
(0.931) (-0.33) (1.243) (-0.512) (1.083) (0.452) (2.770) (2.630) (-0.335)
Hong Kong-Beijing (H-B) 0.023 0.050 0.021 -0.003 0.024 -0.004 0.200 0.030 0.021
(3.833) (1.59) 3.475 (-0.187) (3.811) (-0.200) (3.010) (1.880) (3.297)
Hong Kong-Hong Kong (H-H) -0.005 0.050 -0.005 -0.014 -0.004 -0.0003 -0.100 -0.020 -0.003
(-0.722) (1.340) (-0.625) (-1.290) (-0.505) (-0.022) (-1.33) (-0.980) (-0.426)